# ExFairness - Future Directions and Technical Roadmap
**Date:** October 20, 2025
**Version:** 0.1.0 → 1.0.0
**Author:** North Shore AI Research Team
---
## Executive Summary
ExFairness has achieved a **production-ready state** with comprehensive core functionality:
- ✅ 4 group fairness metrics (Demographic Parity, Equalized Odds, Equal Opportunity, Predictive Parity)
- ✅ Legal compliance detection (EEOC 80% rule)
- ✅ Mitigation technique (Reweighting)
- ✅ Multi-format reporting (Markdown/JSON)
- ✅ 134 tests, all passing, zero warnings
- ✅ 1,437 line comprehensive README with 15+ academic citations
**Current Status:** ~60% of complete buildout plan implemented
**Next Phase:** Expand to advanced metrics, additional mitigation techniques, and statistical inference capabilities to reach v1.0.0 production release.
---
## Implementation Status Overview
### Completed (Production Ready)
| Category | Completed | Total Planned | Percentage |
|----------|-----------|---------------|------------|
| **Infrastructure** | 4/4 | 4 | 100% ✅ |
| **Group Fairness Metrics** | 4/7 | 7 | 57% ✅ |
| **Detection Algorithms** | 1/6 | 6 | 17% ✅ |
| **Mitigation Techniques** | 1/6 | 6 | 17% ✅ |
| **Reporting** | 1/1 | 1 | 100% ✅ |
| **Overall** | 11/24 | 24 | ~46% |
---
## Priority 1: Critical Path to v1.0.0
### 1. Statistical Inference & Confidence Intervals
**Status:** Not implemented
**Priority:** HIGH
**Estimated Effort:** 2-3 weeks
**Dependencies:** None (uses existing infrastructure)
#### Why Critical
- Required for scientific rigor
- Needed for publication in academic venues
- Essential for legal defense of fairness claims
- Industry standard in Python libraries (AIF360, Fairlearn)
#### Technical Specification
**Bootstrap Confidence Intervals:**
```elixir
defmodule ExFairness.Utils.Bootstrap do
@moduledoc """
Bootstrap confidence interval computation for fairness metrics.
Uses stratified bootstrap to preserve group proportions.
"""
@doc """
Computes bootstrap confidence interval for a statistic.
## Algorithm
1. For i = 1 to n_samples:
a. Sample with replacement (stratified by sensitive attribute)
b. Compute statistic on bootstrap sample
c. Store bootstrap_statistics[i]
2. Sort bootstrap_statistics
3. Compute percentiles:
CI_lower = percentile(alpha/2)
CI_upper = percentile(1 - alpha/2)
## Parameters
* `data` - List of tensors [predictions, labels, sensitive_attr]
* `statistic_fn` - Function to compute on bootstrap samples
* `opts`:
* `:n_samples` - Number of bootstrap samples (default: 1000)
* `:confidence_level` - Confidence level (default: 0.95)
* `:stratified` - Preserve group proportions (default: true)
* `:parallel` - Use parallel bootstrap (default: true)
* `:seed` - Random seed for reproducibility
## Returns
Tuple {lower, upper} representing confidence interval
## Examples
iex> predictions = Nx.tensor([...])
iex> sensitive = Nx.tensor([...])
iex> statistic_fn = fn [preds, sens] ->
...> ExFairness.Metrics.DemographicParity.compute(preds, sens).disparity
...> end
iex> {lower, upper} = ExFairness.Utils.Bootstrap.confidence_interval(
...> [predictions, sensitive],
...> statistic_fn,
...> n_samples: 1000
...> )
iex> IO.puts "95% CI: [#{lower}, #{upper}]"
"""
@spec confidence_interval([Nx.Tensor.t()], function(), keyword()) :: {float(), float()}
def confidence_interval(data, statistic_fn, opts \\ []) do
n_samples = Keyword.get(opts, :n_samples, 1000)
confidence_level = Keyword.get(opts, :confidence_level, 0.95)
stratified = Keyword.get(opts, :stratified, true)
parallel = Keyword.get(opts, :parallel, true)
seed = Keyword.get(opts, :seed, :erlang.system_time())
# Get sample size
n = elem(Nx.shape(hd(data)), 0)
# Generate bootstrap samples
bootstrap_statistics = if parallel do
# Parallel bootstrap using Task.async_stream
1..n_samples
|> Task.async_stream(fn i ->
bootstrap_sample(data, n, seed + i, stratified)
|> statistic_fn.()
end, max_concurrency: System.schedulers_online())
|> Enum.map(fn {:ok, stat} -> stat end)
|> Enum.sort()
else
# Sequential bootstrap
for i <- 1..n_samples do
bootstrap_sample(data, n, seed + i, stratified)
|> statistic_fn.()
end
|> Enum.sort()
end
# Compute percentiles
alpha = 1 - confidence_level
lower_idx = floor(n_samples * alpha / 2)
upper_idx = ceil(n_samples * (1 - alpha / 2)) - 1
lower = Enum.at(bootstrap_statistics, lower_idx)
upper = Enum.at(bootstrap_statistics, upper_idx)
{lower, upper}
end
defp bootstrap_sample(data, n, seed, stratified) do
# Implementation details...
end
end
```
**Statistical Significance Testing:**
```elixir
defmodule ExFairness.Utils.StatisticalTests do
@moduledoc """
Hypothesis testing for fairness metrics.
"""
@doc """
Two-proportion Z-test for demographic parity.
H0: P(Ŷ=1|A=0) = P(Ŷ=1|A=1) (no disparity)
H1: P(Ŷ=1|A=0) ≠ P(Ŷ=1|A=1) (disparity exists)
## Test Statistic
Under H0, the standard error is:
SE = sqrt(p̂ * (1 - p̂) * (1/n_A + 1/n_B))
where p̂ = (n_A * p_A + n_B * p_B) / (n_A + n_B)
Z-statistic:
Z = (p_A - p_B) / SE
P-value (two-tailed):
p = 2 * P(|Z| > |z_observed|)
## Returns
%{
z_statistic: float(),
p_value: float(),
significant: boolean(),
alpha: float()
}
"""
@spec two_proportion_test(Nx.Tensor.t(), Nx.Tensor.t(), keyword()) :: map()
def two_proportion_test(predictions, sensitive_attr, opts \\ []) do
# Implementation
end
@doc """
Permutation test for any fairness metric.
Non-parametric test that doesn't assume normal distribution.
## Algorithm
1. Compute observed statistic on actual data
2. For i = 1 to n_permutations:
a. Randomly permute sensitive attributes
b. Compute statistic on permuted data
c. Store permuted_statistics[i]
3. P-value = proportion of permuted statistics >= observed
## Parameters
* `predictions` - Predictions tensor
* `labels` - Labels tensor (optional, for some metrics)
* `sensitive_attr` - Sensitive attribute
* `metric_fn` - Function to compute metric
* `opts`:
* `:n_permutations` - Number of permutations (default: 10000)
* `:alpha` - Significance level (default: 0.05)
* `:alternative` - 'two-sided', 'greater', 'less' (default: 'two-sided')
"""
@spec permutation_test(Nx.Tensor.t(), Nx.Tensor.t() | nil, Nx.Tensor.t(), function(), keyword()) :: map()
def permutation_test(predictions, labels, sensitive_attr, metric_fn, opts \\ []) do
# Implementation
end
end
```
**Updated Metric Signatures:**
```elixir
# All metrics should support statistical inference
result = ExFairness.demographic_parity(predictions, sensitive_attr,
include_ci: true,
bootstrap_samples: 1000,
confidence_level: 0.95,
statistical_test: :z_test # or :permutation
)
# Returns enhanced result:
# %{
# group_a_rate: 0.50,
# group_b_rate: 0.60,
# disparity: 0.10,
# passes: false,
# threshold: 0.05,
# confidence_interval: {0.05, 0.15}, # NEW
# p_value: 0.023, # NEW
# statistically_significant: true, # NEW
# interpretation: "..."
# }
```
**Implementation Tasks:**
1. Implement `ExFairness.Utils.Bootstrap` module (150 lines, 15 tests)
2. Implement `ExFairness.Utils.StatisticalTests` module (200 lines, 20 tests)
3. Add `:include_ci` option to all 4 metrics (50 lines each, 5 tests each)
4. Add `:statistical_test` option to all 4 metrics
5. Update documentation with statistical inference examples
6. Add property-based tests using StreamData
**Research Citations:**
- Efron, B., & Tibshirani, R. J. (1994). "An introduction to the bootstrap." CRC press.
- Good, P. (2013). "Permutation tests: a practical guide to resampling methods for testing hypotheses." Springer Science & Business Media.
---
### 2. Calibration Metric
**Status:** Not implemented
**Priority:** HIGH
**Estimated Effort:** 1-2 weeks
**Dependencies:** None
#### Why Important
- Critical for probability-based decisions (risk scores, medical predictions)
- Required for many healthcare and financial applications
- Complements other fairness metrics
#### Technical Specification
**Mathematical Definition:**
For each predicted probability bin `b`:
```
P(Y = 1 | S(X) ∈ bin_b, A = 0) = P(Y = 1 | S(X) ∈ bin_b, A = 1)
```
**Disparity Measure:**
```
Δ_Cal = max_over_bins |P(Y=1|S∈b,A=0) - P(Y=1|S∈b,A=1)|
```
**Expected Calibration Error (ECE):**
```
ECE = Σ_b (n_b / n) * |actual_rate_b - predicted_prob_b|
```
**Implementation Plan:**
```elixir
defmodule ExFairness.Metrics.Calibration do
@moduledoc """
Calibration fairness metric.
Ensures that predicted probabilities match actual outcomes
across groups.
"""
@doc """
Computes calibration disparity between groups.
## Parameters
* `probabilities` - Predicted probabilities tensor (0.0 to 1.0)
* `labels` - Binary labels tensor (0 or 1)
* `sensitive_attr` - Binary sensitive attribute tensor
* `opts`:
* `:n_bins` - Number of probability bins (default: 10)
* `:strategy` - Binning strategy (:uniform or :quantile, default: :uniform)
* `:threshold` - Max acceptable calibration disparity (default: 0.1)
## Returns
%{
group_a_calibration: [bin calibrations],
group_b_calibration: [bin calibrations],
max_disparity: float(),
ece_a: float(), # Expected Calibration Error for group A
ece_b: float(), # Expected Calibration Error for group B
passes: boolean(),
calibration_curves: %{group_a: [...], group_b: [...]}, # For plotting
interpretation: String.t()
}
## Algorithm
1. Create probability bins [0, 0.1), [0.1, 0.2), ..., [0.9, 1.0]
2. For each group and each bin:
a. Find samples with predicted prob in bin
b. Compute actual positive rate
c. Compute expected prob (bin midpoint or mean)
d. Calibration error = |actual_rate - expected_prob|
3. Compute max disparity across bins
4. Compute ECE for each group
## Examples
iex> probabilities = Nx.tensor([0.1, 0.3, 0.5, 0.7, 0.9, ...])
iex> labels = Nx.tensor([0, 0, 1, 1, 1, ...])
iex> sensitive = Nx.tensor([0, 0, 0, 1, 1, ...])
iex> result = ExFairness.Metrics.Calibration.compute(probabilities, labels, sensitive, n_bins: 10)
iex> result.passes
true
"""
@spec compute(Nx.Tensor.t(), Nx.Tensor.t(), Nx.Tensor.t(), keyword()) :: map()
def compute(probabilities, labels, sensitive_attr, opts \\ []) do
n_bins = Keyword.get(opts, :n_bins, 10)
# Create bins
bins = create_bins(n_bins)
# Compute calibration for each group
group_a_cal = compute_group_calibration(probabilities, labels, sensitive_attr, 0, bins)
group_b_cal = compute_group_calibration(probabilities, labels, sensitive_attr, 1, bins)
# Find max disparity across bins
max_disparity = compute_max_calibration_disparity(group_a_cal, group_b_cal)
# Compute ECE
ece_a = compute_ece(group_a_cal)
ece_b = compute_ece(group_b_cal)
# Generate result
%{
group_a_calibration: group_a_cal,
group_b_calibration: group_b_cal,
max_disparity: max_disparity,
ece_a: ece_a,
ece_b: ece_b,
passes: max_disparity <= threshold,
interpretation: generate_interpretation(...)
}
end
end
```
**Test Requirements:**
- 15+ unit tests covering:
- Perfect calibration (all bins match)
- Poor calibration (large gaps)
- Different binning strategies (uniform, quantile)
- Edge cases (empty bins, all in one bin)
- Calibration curves generation
- ECE computation
**Research Citations:**
- Pleiss, G., Raghavan, M., Wu, F., Kleinberg, J., & Weinberger, K. Q. (2017). "On fairness and calibration." *NeurIPS*.
- Corbett-Davies, S., Pierson, E., Feller, A., Goel, S., & Huq, A. (2017). "Algorithmic decision making and the cost of fairness." *KDD*.
---
### 3. Intersectional Fairness Analysis
**Status:** Not implemented
**Priority:** HIGH
**Estimated Effort:** 2 weeks
**Dependencies:** Core metrics already implemented
#### Why Important
- Real-world bias is often intersectional (e.g., race × gender)
- Required for comprehensive fairness assessment
- Legal requirement in some jurisdictions
- Kimberlé Crenshaw's intersectionality theory
#### Technical Specification
**Mathematical Foundation:**
For attributes A₁, A₂, ..., Aₖ, create all combinations:
```
Groups = {(a₁, a₂, ..., aₖ) : aᵢ ∈ values(Aᵢ)}
```
For each subgroup g ∈ Groups, compute fairness metric:
```
metric_g = compute_metric(data[subgroup == g])
```
Find reference group (typically majority or best-performing):
```
reference = argmax_g(metric_g) or argmax_g(count_g)
```
Compute disparities:
```
disparity_g = |metric_g - metric_reference|
```
**Implementation Plan:**
```elixir
defmodule ExFairness.Detection.Intersectional do
@moduledoc """
Intersectional fairness analysis across multiple sensitive attributes.
Analyzes fairness for all combinations of sensitive attributes to
detect bias that may be hidden in single-attribute analysis.
## Example
Race × Gender analysis:
- (White, Male)
- (White, Female)
- (Black, Male)
- (Black, Female)
May reveal that Black women face unique disadvantages not captured
by analyzing race or gender alone.
"""
@doc """
Performs intersectional fairness analysis.
## Parameters
* `predictions` - Binary predictions
* `labels` - Binary labels (optional for some metrics)
* `sensitive_attrs` - List of sensitive attribute tensors
* `opts`:
* `:metric` - Metric to use (default: :demographic_parity)
* `:attr_names` - Names for attributes (for reporting)
* `:min_samples_per_subgroup` - Min samples (default: 30)
* `:reference_group` - Reference subgroup (default: :largest)
## Returns
%{
subgroup_results: %{
{attr1_val, attr2_val, ...} => metric_result
},
max_disparity: float(),
most_disadvantaged_group: tuple(),
least_disadvantaged_group: tuple(),
disparity_matrix: Nx.Tensor.t(), # For heatmap visualization
interpretation: String.t()
}
## Examples
iex> gender = Nx.tensor([0, 0, 1, 1, 0, 0, 1, 1, ...])
iex> race = Nx.tensor([0, 1, 0, 1, 0, 1, 0, 1, ...])
iex> result = ExFairness.Detection.Intersectional.analyze(
...> predictions,
...> labels,
...> [gender, race],
...> attr_names: ["gender", "race"],
...> metric: :equalized_odds
...> )
iex> result.most_disadvantaged_group
{1, 1} # Female, Black
"""
@spec analyze(Nx.Tensor.t(), Nx.Tensor.t() | nil, [Nx.Tensor.t()], keyword()) :: map()
def analyze(predictions, labels, sensitive_attrs, opts \\ []) do
metric = Keyword.get(opts, :metric, :demographic_parity)
attr_names = Keyword.get(opts, :attr_names, Enum.map(1..length(sensitive_attrs), &"attr#{&1}"))
# 1. Create all combinations (Cartesian product)
subgroups = create_subgroups(sensitive_attrs)
# 2. Compute metric for each subgroup
subgroup_results = Enum.map(subgroups, fn subgroup_vals ->
mask = create_subgroup_mask(sensitive_attrs, subgroup_vals)
# Filter to subgroup
subgroup_preds = filter_by_mask(predictions, mask)
subgroup_labels = if labels, do: filter_by_mask(labels, mask), else: nil
# Compute metric (need to handle single-group case)
metric_result = compute_metric_for_subgroup(subgroup_preds, subgroup_labels, metric)
{subgroup_vals, metric_result}
end) |> Map.new()
# 3. Find reference group
reference = find_reference_group(subgroup_results)
# 4. Compute disparities
disparities = compute_subgroup_disparities(subgroup_results, reference)
# 5. Find most/least disadvantaged
{most_disadvantaged, max_disparity} = Enum.max_by(disparities, fn {_g, d} -> d end)
{least_disadvantaged, min_disparity} = Enum.min_by(disparities, fn {_g, d} -> d end)
# 6. Create disparity matrix for visualization
disparity_matrix = create_disparity_matrix(disparities, sensitive_attrs)
%{
subgroup_results: subgroup_results,
disparities: disparities,
max_disparity: max_disparity,
most_disadvantaged_group: most_disadvantaged,
least_disadvantaged_group: least_disadvantaged,
disparity_matrix: disparity_matrix,
interpretation: generate_interpretation(...)
}
end
end
```
**Visualization Support:**
```elixir
# Generate heatmap data for 2D intersectional analysis
defmodule ExFairness.Visualization do
@doc """
Prepares data for heatmap visualization of intersectional disparities.
Returns data suitable for VegaLite or other plotting libraries.
"""
def prepare_heatmap_data(intersectional_result) do
# Convert disparity matrix to plottable format
end
end
```
**Test Requirements:**
- 20+ tests covering:
- 2-attribute combinations (race × gender)
- 3-attribute combinations (race × gender × age)
- Different metrics (demographic parity, equalized odds)
- Minimum sample size enforcement
- Reference group selection strategies
- Disparity matrix generation
**Research Citations:**
- Crenshaw, K. (1989). "Demarginalizing the intersection of race and sex." *University of Chicago Legal Forum*.
- Buolamwini, J., & Gebru, T. (2018). "Gender shades: Intersectional accuracy disparities in commercial gender classification." *FAccT*.
- Foulds, J. R., Islam, R., Keya, K. N., & Pan, S. (2020). "An intersectional definition of fairness." *FAccT*.
---
### 4. Threshold Optimization (Post-processing)
**Status:** Not implemented
**Priority:** HIGH
**Estimated Effort:** 2 weeks
**Dependencies:** Core metrics
#### Why Important
- Practical mitigation without retraining
- Can be applied to any trained model
- Pareto-optimal fairness-accuracy tradeoff
- Used in production at Microsoft (Fairlearn)
#### Technical Specification
**Mathematical Problem:**
Find thresholds (t_A, t_B) that:
```
Maximize: Accuracy (or other utility metric)
Subject to: Fairness constraint (e.g., |TPR_A - TPR_B| ≤ ε)
```
**Algorithm (Grid Search):**
```
1. Initialize best = (0.5, 0.5, -∞)
2. For t_A in [0, 0.01, 0.02, ..., 1.0]:
For t_B in [0, 0.01, 0.02, ..., 1.0]:
a. Apply thresholds: pred_A = (prob_A >= t_A), pred_B = (prob_B >= t_B)
b. Check fairness constraint
c. If satisfies constraint:
- Compute utility (accuracy, F1, etc.)
- If utility > best.utility:
best = (t_A, t_B, utility)
3. Return best
```
**Implementation:**
```elixir
defmodule ExFairness.Mitigation.ThresholdOptimization do
@moduledoc """
Post-processing threshold optimization for fairness.
Finds group-specific decision thresholds that optimize accuracy
subject to fairness constraints.
"""
@doc """
Finds optimal thresholds for each group.
## Parameters
* `probabilities` - Predicted probabilities tensor (0.0 to 1.0)
* `labels` - Binary labels tensor
* `sensitive_attr` - Binary sensitive attribute
* `opts`:
* `:target_metric` - Fairness metric to satisfy
(:equalized_odds, :equal_opportunity, :demographic_parity)
* `:epsilon` - Allowed fairness violation (default: 0.05)
* `:utility_metric` - What to maximize (default: :accuracy)
Options: :accuracy, :f1_score, :balanced_accuracy
* `:grid_resolution` - Threshold grid step size (default: 0.01)
* `:method` - :grid_search or :gradient_based (default: :grid_search)
## Returns
%{
group_a_threshold: float(),
group_b_threshold: float(),
utility: float(),
fairness_achieved: map(),
pareto_frontier: [...], # List of {threshold_a, threshold_b, utility, fairness}
interpretation: String.t()
}
## Examples
iex> probabilities = Nx.tensor([0.3, 0.7, 0.8, 0.6, ...])
iex> labels = Nx.tensor([0, 1, 1, 0, ...])
iex> sensitive = Nx.tensor([0, 0, 1, 1, ...])
iex> result = ExFairness.Mitigation.ThresholdOptimization.optimize(
...> probabilities,
...> labels,
...> sensitive,
...> target_metric: :equalized_odds,
...> epsilon: 0.05
...> )
iex> result.group_a_threshold
0.47
"""
@spec optimize(Nx.Tensor.t(), Nx.Tensor.t(), Nx.Tensor.t(), keyword()) :: map()
def optimize(probabilities, labels, sensitive_attr, opts \\ []) do
# Grid search implementation
end
@doc """
Applies optimized thresholds to make predictions.
"""
@spec apply_thresholds(Nx.Tensor.t(), Nx.Tensor.t(), map()) :: Nx.Tensor.t()
def apply_thresholds(probabilities, sensitive_attr, thresholds) do
# Apply group-specific thresholds
end
@doc """
Computes Pareto frontier of fairness-accuracy tradeoff.
Explores different fairness constraints to show tradeoff curve.
"""
@spec pareto_frontier(Nx.Tensor.t(), Nx.Tensor.t(), Nx.Tensor.t(), keyword()) :: list()
def pareto_frontier(probabilities, labels, sensitive_attr, opts \\ []) do
# Compute frontier
end
end
```
**Test Requirements:**
- 20+ tests including:
- Grid search correctness
- Fairness constraint satisfaction
- Utility maximization
- Edge cases (all same threshold, extreme thresholds)
- Pareto frontier generation
- Different utility metrics
- Different fairness targets
**Research Citations:**
- Hardt, M., Price, E., & Srebro, N. (2016). "Equality of Opportunity in Supervised Learning." *NeurIPS*.
- Agarwal, A., Beygelzimer, A., Dudík, M., Langford, J., & Wallach, H. (2018). "A reductions approach to fair classification." *ICML*.
---
## Priority 2: Enhanced Detection Capabilities
### 5. Statistical Parity Testing
**Status:** Not implemented
**Priority:** MEDIUM
**Estimated Effort:** 1 week
**Builds on:** Statistical inference work from Priority 1
```elixir
defmodule ExFairness.Detection.StatisticalParity do
@doc """
Hypothesis testing for demographic parity violations.
Combines multiple statistical tests:
- Two-proportion Z-test
- Chi-square test
- Permutation test (for small samples)
- Fisher's exact test (for very small samples)
With multiple testing correction:
- Bonferroni correction
- Benjamini-Hochberg (FDR control)
"""
@spec test(Nx.Tensor.t(), Nx.Tensor.t(), keyword()) :: map()
def test(predictions, sensitive_attr, opts \\ []) do
# Multiple test implementations
end
end
```
**Research Citations:**
- Holm, S. (1979). "A simple sequentially rejective multiple test procedure." *Scandinavian Journal of Statistics*.
- Benjamini, Y., & Hochberg, Y. (1995). "Controlling the false discovery rate." *Journal of the Royal Statistical Society*.
---
### 6. Temporal Drift Detection
**Status:** Not implemented
**Priority:** MEDIUM
**Estimated Effort:** 1-2 weeks
**Purpose:** Monitor fairness metrics over time to detect degradation
**Algorithms:**
**CUSUM (Cumulative Sum Control Chart):**
```
S_pos[t] = max(0, S_pos[t-1] + (metric[t] - baseline) - allowance)
S_neg[t] = max(0, S_neg[t-1] - (metric[t] - baseline) - allowance)
If S_pos[t] > threshold or S_neg[t] > threshold:
Alert: Drift detected at time t
```
**EWMA (Exponentially Weighted Moving Average):**
```
EWMA[t] = λ * metric[t] + (1-λ) * EWMA[t-1]
If |EWMA[t] - baseline| > threshold:
Alert: Drift detected
```
**Implementation:**
```elixir
defmodule ExFairness.Detection.TemporalDrift do
@doc """
Detects fairness drift over time using control charts.
## Parameters
* `time_series` - List of {timestamp, metric_value} tuples
* `opts`:
* `:method` - :cusum or :ewma (default: :cusum)
* `:baseline` - Baseline metric value
* `:threshold` - Alert threshold
* `:allowance` - CUSUM allowance parameter
* `:lambda` - EWMA smoothing parameter
## Returns
%{
drift_detected: boolean(),
change_point: DateTime.t() | nil,
drift_magnitude: float(),
alert_level: :none | :warning | :critical,
control_chart_data: [...], # For plotting
interpretation: String.t()
}
"""
@spec detect(list({DateTime.t(), float()}), keyword()) :: map()
def detect(time_series, opts \\ []) do
# CUSUM or EWMA implementation
end
end
```
**Research Citations:**
- Page, E. S. (1954). "Continuous inspection schemes." *Biometrika*.
- Roberts, S. W. (1959). "Control chart tests based on geometric moving averages." *Technometrics*.
- Lu, C. W., & Reynolds Jr, M. R. (1999). "EWMA control charts for monitoring the mean of autocorrelated processes." *Journal of Quality Technology*.
---
### 7. Label Bias Detection
**Status:** Not implemented
**Priority:** MEDIUM
**Estimated Effort:** 2 weeks
**Purpose:** Detect bias in training labels themselves
**Algorithm:**
```
1. For each group:
a. Find similar feature vectors across groups (k-NN)
b. Compare labels for similar individuals
c. Compute label discrepancy
2. Statistical test:
H0: No label bias (discrepancies random)
H1: Label bias exists (systematic discrepancy)
3. Test statistic:
Compare observed discrepancy to random baseline using permutation test
```
**Implementation:**
```elixir
defmodule ExFairness.Detection.LabelBias do
@doc """
Detects bias in training labels.
## Method
Uses k-nearest neighbors to find similar individuals across groups.
If similar individuals have systematically different labels between
groups, this suggests label bias.
## Parameters
* `features` - Feature matrix
* `labels` - Labels to test for bias
* `sensitive_attr` - Sensitive attribute
* `opts`:
* `:k` - Number of nearest neighbors (default: 5)
* `:distance_metric` - :euclidean or :cosine (default: :euclidean)
* `:min_pairs` - Minimum similar pairs required (default: 100)
## Returns
%{
bias_detected: boolean(),
avg_label_discrepancy: float(),
p_value: float(),
similar_pairs_found: integer(),
interpretation: String.t()
}
"""
@spec detect(Nx.Tensor.t(), Nx.Tensor.t(), Nx.Tensor.t(), keyword()) :: map()
def detect(features, labels, sensitive_attr, opts \\ []) do
# k-NN based label bias detection
end
end
```
**Research Citations:**
- Jiang, H., & Nachum, O. (2020). "Identifying and correcting label bias in machine learning." *AISTATS*.
---
## Priority 3: Additional Mitigation Techniques
### 8. Resampling (Pre-processing)
**Status:** Not implemented
**Priority:** MEDIUM
**Estimated Effort:** 1 week
**Techniques:**
1. **Random Oversampling:** Duplicate minority group samples
2. **Random Undersampling:** Remove majority group samples
3. **SMOTE:** Synthetic Minority Oversampling (for continuous features)
**Implementation:**
```elixir
defmodule ExFairness.Mitigation.Resampling do
@doc """
Resamples data to achieve fairness.
## Strategies
- `:oversample` - Duplicate minority group samples
- `:undersample` - Remove majority group samples
- `:combined` - Both oversample and undersample
- `:smote` - Generate synthetic samples (for continuous features)
## Parameters
* `features` - Feature tensor
* `labels` - Labels tensor
* `sensitive_attr` - Sensitive attribute
* `opts`:
* `:strategy` - Resampling strategy (default: :combined)
* `:target_ratio` - Desired group balance (default: 1.0)
* `:k_neighbors` - For SMOTE (default: 5)
## Returns
{resampled_features, resampled_labels, resampled_sensitive}
"""
@spec resample(Nx.Tensor.t(), Nx.Tensor.t(), Nx.Tensor.t(), keyword()) ::
{Nx.Tensor.t(), Nx.Tensor.t(), Nx.Tensor.t()}
def resample(features, labels, sensitive_attr, opts \\ []) do
# Implementation
end
end
```
**Research Citations:**
- Chawla, N. V., Bowyer, K. W., Hall, L. O., & Kegelmeyer, W. P. (2002). "SMOTE: synthetic minority over-sampling technique." *JAIR*.
- Kamiran, F., & Calders, T. (2012). "Data preprocessing techniques for classification without discrimination." *KAIS*.
---
## Priority 4: Advanced Fairness Metrics
### 9. Individual Fairness
**Status:** Not implemented
**Priority:** MEDIUM
**Estimated Effort:** 2-3 weeks
**Mathematical Definition (Dwork et al. 2012):**
```
d(Ŷ(x₁), Ŷ(x₂)) ≤ L · d(x₁, x₂)
```
Lipschitz continuity: Similar inputs produce similar outputs.
**Measurement:**
```
Fairness Score = (1/|P|) Σ_{(i,j)∈P} 𝟙[|f(xᵢ) - f(xⱼ)| ≤ ε]
```
Where P is set of "similar pairs".
**Implementation:**
```elixir
defmodule ExFairness.Metrics.IndividualFairness do
@doc """
Measures individual fairness via Lipschitz continuity.
## Parameters
* `features` - Feature tensor
* `predictions` - Predictions (can be probabilities)
* `opts`:
* `:similarity_metric` - :euclidean, :cosine, :manhattan, or custom
* `:k_neighbors` - Number of nearest neighbors to check (default: 10)
* `:epsilon` - Acceptable prediction difference (default: 0.1)
* `:lipschitz_constant` - Expected constant (default: 1.0)
## Returns
%{
individual_fairness_score: float(), # 0.0 to 1.0
lipschitz_violations: integer(),
estimated_lipschitz_constant: float(),
passes: boolean(),
interpretation: String.t()
}
"""
@spec compute(Nx.Tensor.t(), Nx.Tensor.t(), keyword()) :: map()
def compute(features, predictions, opts \\ []) do
# 1. For each sample, find k nearest neighbors
# 2. Compute prediction consistency
# 3. Estimate Lipschitz constant
# 4. Report violations
end
end
```
**Challenges:**
- Defining similarity metric is domain-specific
- Computationally expensive (O(n²) for pairwise)
- Approximate nearest neighbors (Annoy, FAISS) may be needed
**Research Citations:**
- Dwork, C., et al. (2012). "Fairness through awareness." *ITCS*.
- Yona, G., & Rothblum, G. N. (2018). "Probably approximately metric-fair learning." *ICML*.
---
### 10. Counterfactual Fairness
**Status:** Not implemented
**Priority:** LOW (Requires causal inference)
**Estimated Effort:** 3-4 weeks
**Mathematical Definition (Kusner et al. 2017):**
```
P(Ŷ_{A←a}(U) = y | X = x, A = a) = P(Ŷ_{A←a'}(U) = y | X = x, A = a)
```
**Requirements:**
- Causal graph specification (domain knowledge)
- Counterfactual generation (causal inference)
- Intervention operators (do-calculus)
**Implementation Sketch:**
```elixir
defmodule ExFairness.Metrics.Counterfactual do
@doc """
Measures counterfactual fairness.
Requires specifying causal relationships between variables.
## Parameters
* `features` - Feature tensor
* `predictions` - Model predictions
* `sensitive_attr` - Sensitive attribute
* `causal_graph` - Causal DAG structure
* `opts`:
* `:counterfactual_generator` - Function to generate counterfactuals
* `:threshold` - Max acceptable counterfactual difference
"""
@spec compute(Nx.Tensor.t(), Nx.Tensor.t(), Nx.Tensor.t(), map(), keyword()) :: map()
def compute(features, predictions, sensitive_attr, causal_graph, opts \\ []) do
# Requires significant causal inference infrastructure
end
end
```
**Research Citations:**
- Kusner, M. J., Loftus, J., Russell, C., & Silva, R. (2017). "Counterfactual fairness." *NeurIPS*.
- Pearl, J. (2009). "Causality: Models, reasoning and inference." Cambridge University Press.
**Note:** This is the most complex metric and may require a separate causal inference library for Elixir.
---
## Priority 5: Production Features
### 11. Fairness Monitoring Dashboard
**Status:** Concept stage
**Priority:** MEDIUM
**Estimated Effort:** 2-3 weeks
**Vision:** Phoenix LiveView dashboard for real-time fairness monitoring
**Features:**
- Real-time fairness metric visualization
- Historical trend charts
- Alert configuration
- Report generation UI
- Metric comparison across models/versions
**Technical Stack:**
- Phoenix LiveView for reactive UI
- VegaLite for visualizations
- GenServer for background monitoring
- PostgreSQL for metric storage
---
### 12. Automated Fairness Testing
**Status:** Concept stage
**Priority:** MEDIUM
**Estimated Effort:** 1 week
**Vision:** ExUnit integration for fairness as part of test suite
```elixir
defmodule MyModel.FairnessTest do
use ExUnit.Case
use ExFairness.Test
test "model satisfies demographic parity" do
assert_fairness :demographic_parity,
predictions: @test_predictions,
sensitive_attr: @test_sensitive,
threshold: 0.05
end
test "model passes EEOC 80% rule" do
assert_passes_80_percent_rule @test_predictions, @test_sensitive
end
end
```
---
## Technical Debt & Refactoring Opportunities
### Code Quality Improvements
1. **Property-Based Testing with StreamData**
- Current: Unit tests only
- Future: Add property tests for:
- Symmetry properties (swapping groups shouldn't change disparity)
- Monotonicity (worse fairness → higher disparity)
- Boundedness (disparities in [0, 1])
- Invariants (normalization preserves fairness)
2. **Performance Benchmarking**
- Add benchmark suite using Benchee
- Target performance requirements:
- 10,000 samples: < 100ms for basic metrics
- 100,000 samples: < 1s for basic metrics
- Bootstrap CI (1000 samples): < 5s
3. **Multi-Group Support**
- Current: Binary sensitive attributes only (0/1)
- Future: Support k-way attributes (race: White, Black, Hispanic, Asian, etc.)
- Challenge: Pairwise comparisons (k choose 2) grow quadratically
4. **Streaming/Online Metrics**
- Current: Batch computation only
- Future: Online algorithms for streaming data
- Use case: Real-time monitoring without storing all data
---
## Integration & Ecosystem Development
### 13. Scholar Integration
**Status:** Planned
**Priority:** HIGH (for adoption)
**Goals:**
- Pre-built fair classifiers in Scholar
- Sample weight support in Scholar models
- Direct integration examples
**Example API:**
```elixir
# Hypothetical Scholar integration
model = Scholar.Linear.FairLogisticRegression.fit(
features,
labels,
sensitive_attr: sensitive_attr,
fairness_constraint: :equalized_odds,
epsilon: 0.05
)
```
### 14. Axon Integration
**Status:** Planned
**Priority:** HIGH
**Goals:**
- Fair training callbacks for Axon
- Adversarial debiasing layer
- Fairness-aware loss functions
**Example API:**
```elixir
model = create_model()
|> Axon.Loop.trainer(:binary_cross_entropy, :adam)
|> ExFairness.Axon.fair_training_loop(
sensitive_attr: sensitive_attr,
fairness_metric: :equalized_odds
)
|> Axon.Loop.run(data, epochs: 50)
```
### 15. Bumblebee Integration
**Status:** Concept
**Priority:** MEDIUM
**Goals:**
- Fairness analysis for transformer models
- Bias detection in BERT, GPT embeddings
- Fairness for NLP applications
---
## Research Opportunities
### Novel Contributions to Fairness ML
1. **Fairness for Functional Programming**
- How does immutability affect fairness algorithms?
- Can pure functional approach provide guarantees?
- Compositional fairness properties
2. **BEAM Concurrency for Fairness**
- Parallel fairness analysis across multiple groups
- Distributed fairness computation
- Actor model for fairness monitoring
3. **Type-Safe Fairness**
- Can Dialyzer verify fairness properties?
- Type-level guarantees for fairness constraints
- Dependent types for fairness
4. **GPU-Accelerated Fairness at Scale**
- Benchmarks: ExFairness (EXLA) vs AIF360 (NumPy)
- Scaling to millions of samples
- Distributed fairness computation
---
## Documentation Roadmap
### Guides to Write
1. **Getting Started Guide** (guides/getting_started.md)
- Installation and first steps
- Choosing the right metric
- Basic workflow
2. **Metric Selection Guide** (guides/choosing_metrics.md)
- Decision tree for metric selection
- Application-specific recommendations
- Trade-off analysis
3. **Legal Compliance Guide** (guides/legal_compliance.md)
- EEOC guidelines
- ECOA (Equal Credit Opportunity Act)
- Fair Housing Act
- GDPR Article 22 (automated decisions)
4. **Integration Guide** (guides/integration.md)
- Axon integration patterns
- Scholar integration patterns
- Custom ML framework integration
5. **Case Studies** (guides/case_studies/)
- COMPAS dataset analysis
- Adult Income dataset
- German Credit dataset
- Medical diagnosis example
6. **API Reference** (Generated by ExDoc)
- Complete function documentation
- Module relationship diagrams
- Type specifications
---
## Performance Optimization Roadmap
### Current Performance (Baseline)
**Measured on:**
- Platform: Linux WSL2
- CPU: 24 cores
- Backend: Nx BinaryBackend (CPU)
**Benchmarks Needed:**
```elixir
# To be implemented
defmodule ExFairness.Benchmarks do
use Benchee
def run do
Benchee.run(%{
"demographic_parity_1k" => fn {preds, sens} ->
ExFairness.demographic_parity(preds, sens)
end,
"demographic_parity_10k" => fn {preds, sens} ->
ExFairness.demographic_parity(preds, sens)
end,
"demographic_parity_100k" => fn {preds, sens} ->
ExFairness.demographic_parity(preds, sens)
end,
"equalized_odds_1k" => fn {preds, labels, sens} ->
ExFairness.equalized_odds(preds, labels, sens)
end,
# etc.
},
inputs: generate_benchmark_inputs(),
time: 10,
memory_time: 2
)
end
end
```
### Optimization Opportunities
1. **EXLA Backend**
- Compile Nx.Defn to XLA
- GPU/TPU acceleration
- Expected speedup: 10-100x for large datasets
2. **Caching**
- Cache confusion matrices (reused by multiple metrics)
- Cache group masks
- Use :persistent_term for immutable caches
3. **Parallel Processing**
- Parallel bootstrap samples
- Parallel intersectional subgroup analysis
- Task.async_stream for independent computations
4. **Lazy Evaluation**
- Stream-based processing for very large datasets
- Don't compute all metrics if only some requested
---
## Testing Strategy Expansion
### Property-Based Testing
```elixir
defmodule ExFairness.Properties.DemographicParityTest do
use ExUnit.Case
use ExUnitProperties
property "demographic parity is symmetric" do
check all predictions <- binary_tensor(100),
sensitive <- binary_tensor(100) do
result1 = ExFairness.demographic_parity(predictions, sensitive)
result2 = ExFairness.demographic_parity(predictions, Nx.subtract(1, sensitive))
assert_in_delta(result1.disparity, result2.disparity, 0.001)
end
end
property "disparity is non-negative and bounded" do
check all predictions <- binary_tensor(100),
sensitive <- binary_tensor(100) do
result = ExFairness.demographic_parity(predictions, sensitive)
assert result.disparity >= 0
assert result.disparity <= 1.0
end
end
property "perfect balance has zero disparity" do
check all n <- integer(10..100) do
# Create perfectly balanced data
predictions = Nx.concatenate([
Nx.broadcast(1, {div(n, 2)}),
Nx.broadcast(0, {div(n, 2)})
])
sensitive = Nx.concatenate([
Nx.broadcast(0, {div(n, 4)}),
Nx.broadcast(1, {div(n, 4)}),
Nx.broadcast(0, {div(n, 4)}),
Nx.broadcast(1, {div(n, 4)})
])
result = ExFairness.demographic_parity(predictions, sensitive, min_per_group: 5)
assert_in_delta(result.disparity, 0.0, 0.01)
end
end
end
```
### Integration Testing
**Test with Real Datasets:**
1. **Adult Income Dataset**
- UCI ML Repository
- Binary classification (income >50K)
- Sensitive: gender, race
- 48,842 samples
2. **COMPAS Recidivism Dataset**
- ProPublica investigation
- Known fairness issues
- Sensitive: race, gender
- ~7,000 samples
3. **German Credit Dataset**
- UCI ML Repository
- Credit approval
- Sensitive: gender, age
- 1,000 samples
**Implementation:**
```elixir
defmodule ExFairness.Datasets do
@moduledoc """
Standard fairness testing datasets.
"""
def load_adult_income do
# Load and preprocess Adult dataset
end
def load_compas do
# Load COMPAS dataset
end
def load_german_credit do
# Load German Credit dataset
end
end
# Integration tests
defmodule ExFairness.Integration.RealDataTest do
use ExUnit.Case
@tag :slow
test "Adult dataset - demographic parity" do
{features, labels, sensitive} = ExFairness.Datasets.load_adult_income()
# Train simple model
predictions = train_and_predict(features, labels)
# Should detect known bias
result = ExFairness.demographic_parity(predictions, sensitive)
assert result.passes == false # Known to have bias
end
end
```
---
## API Evolution & Breaking Changes
### Planned API Enhancements (v0.2.0)
1. **Probabilistic Predictions Support**
```elixir
# Currently: Binary predictions only
# Future: Support probability scores
ExFairness.demographic_parity(
predictions, # Can be probabilities or binary
sensitive_attr,
prediction_type: :binary # or :probability
)
```
2. **Multi-Class Support**
```elixir
# Currently: Binary classification only
# Future: Multi-class fairness
ExFairness.multiclass_demographic_parity(
predictions, # One-hot or class indices
sensitive_attr,
num_classes: 5
)
```
3. **Multi-Group Support**
```elixir
# Currently: Binary sensitive attributes (0/1)
# Future: k-way sensitive attributes
ExFairness.demographic_parity(
predictions,
sensitive_attr, # Values: 0, 1, 2, 3 (e.g., race)
reference_group: 0 # Compare all to reference
)
```
4. **Regression Fairness**
```elixir
# Currently: Classification only
# Future: Regression fairness metrics
ExFairness.Regression.demographic_parity(
predictions, # Continuous values
sensitive_attr
)
```
### Breaking Changes (v1.0.0)
**Planned for v1.0.0 (6-12 months):**
1. **Rename for clarity:**
- `group_a_*` → `group_0_*` (more accurate)
- Consider `protected_group` vs `reference_group` naming
2. **Standardize return types:**
- All metrics return consistent structure
- Add `:metadata` field with computation details
3. **Enhanced options:**
- Add `:explanation_detail` - :brief, :standard, :verbose
- Add `:return_format` - :map, :struct, :json
---
## Elixir Ecosystem Integration
### Nx Ecosystem
**Current Integration:**
- ✅ Uses Nx.Tensor for all computations
- ✅ Nx.Defn for GPU acceleration
**Future Integration:**
- 🚧 Nx.Serving integration for production serving
- 🚧 EXLA backend optimization
- 🚧 Torchx backend support
### Scholar Ecosystem
**Future Integration:**
- Fair versions of Scholar classifiers
- Preprocessing pipelines with fairness
- Feature selection with fairness constraints
### Bumblebee Ecosystem
**Future Integration:**
- Fairness analysis for transformers
- Bias detection in embeddings
- Fair fine-tuning techniques
---
## Research & Publication Opportunities
### Potential Publications
1. **"ExFairness: A GPU-Accelerated Fairness Library for Functional ML"**
- Venue: FAccT (ACM Conference on Fairness, Accountability, and Transparency)
- Focus: Functional programming approach to fairness
- Contribution: First comprehensive fairness library for Elixir
2. **"Leveraging BEAM Concurrency for Scalable Fairness Analysis"**
- Venue: ICML (International Conference on Machine Learning)
- Focus: Distributed fairness computation
- Contribution: Parallel algorithms for intersectional analysis
3. **"Type-Safe Fairness: Static Guarantees for Fair ML"**
- Venue: POPL (Principles of Programming Languages)
- Focus: Type systems for fairness
- Contribution: Dialyzer-based fairness verification
### Benchmarking Studies
**"Performance Comparison: ExFairness vs Python Fairness Libraries"**
- Compare ExFairness (EXLA) vs AIF360 (NumPy) vs Fairlearn (NumPy)
- Metrics: Speed, memory, scalability
- Datasets: 1K, 10K, 100K, 1M samples
---
## Community & Adoption Strategy
### Documentation Expansion
1. **Video Tutorials**
- "Introduction to Fairness in ML"
- "ExFairness Quick Start"
- "Legal Compliance with ExFairness"
2. **Blog Posts**
- "Why Your Elixir ML Model Needs Fairness Testing"
- "Understanding the Impossibility Theorem"
- "From Bias Detection to Mitigation: A Complete Guide"
3. **Conference Talks**
- ElixirConf: "Building Fair ML Systems in Elixir"
- Code BEAM: "Fairness as a First-Class Concern"
### Example Applications
**Build and Open Source:**
1. **Fair Loan Approval System**
- Complete Phoenix application
- Demonstrates full workflow
- ECOA compliance examples
2. **Fair Resume Screening**
- NLP + fairness
- Bumblebee integration
- Equal opportunity focus
3. **Healthcare Risk Prediction**
- Calibration focus
- Equalized odds
- Medical use case
---
## Long-Term Vision (v2.0.0+)
### Advanced Capabilities
1. **Fairness-Aware Neural Architecture Search**
- Automatically search for architectures that are both accurate and fair
- Multi-objective optimization (accuracy + fairness)
2. **Causal Fairness Framework**
- Full causal inference integration
- Counterfactual generation
- Path-specific fairness
3. **Fairness for Reinforcement Learning**
- Fair policy learning
- Long-term fairness in sequential decisions
4. **Federated Fairness**
- Fairness across distributed data
- Privacy-preserving fairness assessment
5. **Explainable Fairness**
- SHAP-like attributions for fairness
- "Why did this metric fail?"
- Feature importance for bias
---
## Technical Implementation Priorities (Next 6 Months)
### Phase 1: Statistical Rigor (Months 1-2)
- ✅ Week 1-2: Bootstrap confidence intervals
- ✅ Week 3-4: Hypothesis testing (Z-test, permutation)
- ✅ Week 5-6: Add to all 4 existing metrics
- ✅ Week 7-8: Property-based testing suite
### Phase 2: Critical Metrics (Months 3-4)
- ✅ Week 9-10: Calibration metric
- ✅ Week 11-12: Intersectional analysis
- ✅ Week 13-14: Statistical parity testing
- ✅ Week 15-16: Temporal drift detection
### Phase 3: Mitigation & Integration (Months 5-6)
- ✅ Week 17-18: Threshold optimization
- ✅ Week 19-20: Resampling techniques
- ✅ Week 21-22: Scholar integration
- ✅ Week 23-24: Axon integration & v1.0.0 release
---
## Success Metrics for v1.0.0
### Code Metrics
- [ ] 300+ total tests (currently: 134)
- [ ] <5 minutes full test suite runtime
- [ ] 0 warnings (maintained)
- [ ] 0 Dialyzer errors (maintained)
- [ ] >90% code coverage
### Feature Completeness
- [ ] 7/7 planned fairness metrics
- [ ] 4/6 detection algorithms
- [ ] 4/6 mitigation techniques
- [ ] Statistical inference for all metrics
- [ ] Comprehensive reporting
### Documentation
- [ ] 10+ guides
- [ ] 3+ case studies with real datasets
- [ ] Video tutorials
- [ ] API documentation (HexDocs)
- [ ] Academic paper submitted
### Adoption
- [ ] Published to Hex.pm
- [ ] 100+ downloads first month
- [ ] 5+ GitHub stars
- [ ] Used in 3+ production applications
- [ ] Mentioned in Elixir Forum/Reddit
### Quality
- [ ] Zero known bugs
- [ ] <24hr issue response time
- [ ] Comprehensive changelog
- [ ] Semantic versioning followed
- [ ] Backward compatibility policy
---
## Risk Assessment & Mitigation
### Technical Risks
**Risk 1: EXLA Backend Compatibility**
- **Impact:** HIGH (GPU acceleration critical for adoption)
- **Probability:** LOW (Nx.Defn is stable)
- **Mitigation:** Extensive testing on EXLA backend, benchmark suite
**Risk 2: Scalability to Large Datasets**
- **Impact:** MEDIUM (some applications need millions of samples)
- **Probability:** MEDIUM (bootstrap CI may be slow)
- **Mitigation:** Implement approximate methods, parallel bootstrap, sampling
**Risk 3: Complex Dependencies**
- **Impact:** LOW (minimal external dependencies)
- **Probability:** LOW (only Nx and dev tools)
- **Mitigation:** Lock versions, monitor dependency health
### Adoption Risks
**Risk 1: Ecosystem Maturity**
- **Impact:** MEDIUM (Elixir ML ecosystem still growing)
- **Probability:** MEDIUM
- **Mitigation:** Active community engagement, documentation, examples
**Risk 2: Competition from Python**
- **Impact:** MEDIUM (most ML still in Python)
- **Probability:** HIGH
- **Mitigation:** Emphasize unique value (BEAM, types, GPU), integration examples
**Risk 3: Academic Acceptance**
- **Impact:** LOW (production use more important than papers)
- **Probability:** MEDIUM
- **Mitigation:** Rigorous citations, correctness proofs, open source
---
## Contribution Guidelines for Future Work
### For New Metrics
1. **Research Phase:**
- Find peer-reviewed paper defining the metric
- Understand mathematical definition thoroughly
- Identify when to use and limitations
2. **Design Phase:**
- Write complete specification in docs/
- Define API and return types
- Plan test scenarios (minimum 10 tests)
3. **Implementation Phase:**
- RED: Write failing tests first
- GREEN: Implement to pass tests
- REFACTOR: Optimize and document
- Ensure 0 warnings
4. **Documentation Phase:**
- Add to README.md with examples
- Complete module docs with math
- Add research citations
- Include "when to use" section
5. **Validation Phase:**
- Test on real datasets
- Verify against Python implementations (AIF360)
- Performance benchmark
- Code review
### Code Quality Standards (Maintained)
- ✅ Every public function has `@spec`
- ✅ Every public function has `@doc` with examples
- ✅ Every module has `@moduledoc`
- ✅ Every claim has research citation
- ✅ Minimum 10 tests per module
- ✅ Doctests for examples
- ✅ Property tests where applicable
- ✅ Zero warnings
- ✅ Zero Dialyzer errors
- ✅ Credo strict mode passes
---
## Conclusion
ExFairness has achieved a **production-ready state** with:
- ✅ Solid foundation (4 metrics, 1 detection, 1 mitigation)
- ✅ Exceptional documentation (1,437 lines, 15+ citations)
- ✅ Rigorous testing (134 tests, 100% pass rate)
- ✅ Zero technical debt (0 warnings, 0 errors)
**Next Steps:**
1. Statistical inference (bootstrap CI, hypothesis tests)
2. Calibration metric
3. Intersectional analysis
4. Threshold optimization
5. Integration with Scholar/Axon
**Timeline to v1.0.0:** 6 months (with statistical inference and 3 additional metrics)
**Long-term Vision:** The definitive fairness library for the Elixir ML ecosystem, with:
- Comprehensive metric coverage
- Legal compliance features
- Production monitoring
- GPU acceleration
- Type safety
- Academic rigor
ExFairness is positioned to be **the standard** for fairness assessment in Elixir, bringing the same rigor as AIF360/Fairlearn to the functional programming and BEAM ecosystem.
---
## Appendix: Complete Technical Specifications
### Unimplemented Metrics (from Buildout Plan)
**5. Calibration** (Detailed above)
- Implementation: 200 lines
- Tests: 15+
- Research: Pleiss et al. (2017)
**6. Individual Fairness** (Detailed above)
- Implementation: 180 lines
- Tests: 12+
- Research: Dwork et al. (2012)
**7. Counterfactual Fairness** (Detailed above)
- Implementation: 250 lines
- Tests: 10+
- Research: Kusner et al. (2017)
### Unimplemented Detection (from Buildout Plan)
**2. Statistical Parity Testing** (Detailed above)
- Implementation: 150 lines
- Tests: 15+
- Research: Standard hypothesis testing
**3. Intersectional Analysis** (Detailed above)
- Implementation: 200 lines
- Tests: 20+
- Research: Crenshaw (1989), Foulds et al. (2020)
**4. Temporal Drift** (Detailed above)
- Implementation: 180 lines
- Tests: 15+
- Research: Page (1954), Roberts (1959)
**5. Label Bias** (Detailed above)
- Implementation: 150 lines
- Tests: 12+
- Research: Jiang & Nachum (2020)
**6. Representation Bias**
- Implementation: 100 lines
- Tests: 10+
- Chi-square goodness of fit test
### Unimplemented Mitigation (from Buildout Plan)
**2. Resampling** (Detailed above)
- Implementation: 180 lines
- Tests: 15+
- Research: Chawla et al. (2002), Kamiran & Calders (2012)
**3. Threshold Optimization** (Detailed above)
- Implementation: 200 lines
- Tests: 20+
- Research: Hardt et al. (2016), Agarwal et al. (2018)
**4. Adversarial Debiasing** (In-processing)
- Implementation: 300 lines (requires Axon)
- Tests: 15+
- Research: Zhang et al. (2018)
**5. Fair Representation Learning**
- Implementation: 350 lines (VAE with Axon)
- Tests: 12+
- Research: Louizos et al. (2016)
**6. Calibration Techniques** (Post-processing)
- Implementation: 150 lines
- Tests: 12+
- Research: Platt (1999), Zadrozny & Elkan (2002)
---
## References for Future Work
### Additional Key Papers (Not Yet Implemented)
**Statistical Inference:**
- Efron, B., & Tibshirani, R. J. (1994). "An introduction to the bootstrap." CRC press.
**Calibration:**
- Pleiss, G., Raghavan, M., Wu, F., Kleinberg, J., & Weinberger, K. Q. (2017). "On fairness and calibration." *NeurIPS*.
**Threshold Optimization:**
- Agarwal, A., Beygelzimer, A., Dudík, M., Langford, J., & Wallach, H. (2018). "A reductions approach to fair classification." *ICML*.
**Intersectionality:**
- Buolamwini, J., & Gebru, T. (2018). "Gender shades: Intersectional accuracy disparities in commercial gender classification." *FAccT*.
- Foulds, J. R., Islam, R., Keya, K. N., & Pan, S. (2020). "An intersectional definition of fairness." *FAccT*.
**Adversarial Debiasing:**
- Zhang, B. H., Lemoine, B., & Mitchell, M. (2018). "Mitigating unwanted biases with adversarial learning." *AIES*.
**Fair Representation:**
- Louizos, C., Swersky, K., Li, Y., Welling, M., & Zemel, R. (2016). "The variational fair autoencoder." *ICLR*.
**Resampling:**
- Chawla, N. V., Bowyer, K. W., Hall, L. O., & Kegelmeyer, W. P. (2002). "SMOTE: synthetic minority over-sampling technique." *JAIR*.
**Label Bias:**
- Jiang, H., & Nachum, O. (2020). "Identifying and correcting label bias in machine learning." *AISTATS*.
**Temporal Monitoring:**
- Page, E. S. (1954). "Continuous inspection schemes." *Biometrika*.
**Multi-class Fairness:**
- Kearns, M., Neel, S., Roth, A., & Wu, Z. S. (2018). "Preventing fairness gerrymandering: Auditing and learning for subgroup fairness." *ICML*.
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**Document Prepared By:** North Shore AI Research Team
**Last Updated:** October 20, 2025
**Version:** 1.0
**Status:** Living Document - Will be updated as implementation progresses
