Bias in Algorithms

Sarah Dean

CS 194/294 Guest Lecture, 3/1/21

About Me

  • PhD student in EECS
  • "Reliable Machine Learning in Feedback Systems"
    • formally reasoning about safety, stability, equity, wellbeing
  • Co-founded GEESE (geesegraduates.org)

Algorithms are consequential and ubiquitous

Bias in Algorithms

Bias: prejudice, usually in a way considered to be unfair

(mathematical) systematic distortion of a statistical result

Prejudice: preconceived opinion

(legal) harm or injury resulting from some action or judgment

Algorithm: a set of rules to be followed, especially by a computer

Promises of algorithmic screening

  • explicit criteria
  • equally applied
  • auditable

Ex: Algorithmic Resume Screening

"I usually look for candidates who will be a good fit"

Ex: Algorithmic Resume Screening

Promises of algorithmic data-driven screening

  • explicit criteria "superhuman" accuracy
  • equally applied
  • auditable

Ex: Data-Driven Resume Screening

Hand-coded algorithms are hard!

Machine learning extracts statistical patterns from employment records to automatically generate accurate and optimal classifications

Does Data-Driven Screening Discriminate?

Federal laws make it illegal to discriminate on the basis of  race, color, religion, sex, national origin, pregnancy, disability, age, and genetic information.

Important quantities:

Formalizing Algorithmic Decisions

features \(X\) and label \(Y\)

(e.g. resume and employment outcome)

classifier \(c(X) = \hat Y\)

(e.g. interview invitation)

  • Accuracy: \(\mathbb P( \hat Y = Y)\)
  • Positive rate: \(\mathbb P( \hat Y = 1)\)
  • Among truly (un)qualified (conditioned on \(Y\))
  • Among positive (negative) decisions (conditioned on \(\hat Y\))

Statistical Classification Criteria

Accuracy
\(\mathbb P( \hat Y = Y)\) = ________


Positive rate
\(\mathbb P( \hat Y = 1)\) = ________

False positive rate
\(\mathbb P( \hat Y = 1\mid Y = 0)\) = ________


False negative rate

\(\mathbb P( \hat Y = 0\mid Y = 1)\) = ________

Positive predictive value
\(\mathbb P( Y = 1\mid\hat Y = 1)\) = ________


Negative predictive value
\(\mathbb P( Y = 0\mid\hat Y = 0)\) = ________

\(X\)

\(Y=1\)

\(Y=0\)

\(c(X)\)

\(3/4\)

\(9/20\)

\(1/5\)

\(3/10\)

\(7/9\)

\(8/11\)

Formal Nondiscrimination Criteria

features \(X\) and label \(Y\)

(e.g. resume and employment outcome)

classifier \(c(X) = \hat Y\)

(e.g. interview invitation)

individual with protected attribute \(A\)

(e.g. race or gender)

Independence: decision does not depend on \(A\)

\(\hat Y \perp A\)

e.g. applicants are accepted at equal rates across gender

Separation: given outcome, decision does not depend on \(A\)

\(\hat Y \perp A~\mid~Y\)

e.g. qualified applicants are accepted at equal rates across gender

Sufficiency: given decision, outcome does not depend on \(A\)

\( Y \perp A~\mid~\hat Y\)

e.g. accepted applicants are qualified at equal rates across gender

Ex: Pre-trial Detention

  • COMPAS: a criminal risk assessment tool used in pretrial release decisions
    • \(X\) survey about defendant
    • \(\hat Y\) designation as high- or low-risk
  • Audit of data from Broward county, FL
    • \(A\) race of defendant
    • \(Y\) recidivism within two years

“Black defendants who did not recidivate over a two-year period were nearly twice as likely to be misclassified. [...] White defendants who re-offended within the next two years were mistakenly labeled low risk almost twice as often.”

Ex: Pre-trial Detention

“In comparison with whites, a slightly lower percentage of blacks were ‘Labeled Higher Risk, But Didn’t Re-Offend.’ [...] A slightly higher percentage of blacks were ‘Labeled Lower Risk, Yet Did Re-Offend.”’

\(\mathbb P(\hat Y = 1\mid Y=0, A=\text{Black})> \mathbb P(\hat Y = 1\mid Y=0, A=\text{White})  \)

\(\mathbb P(\hat Y = 0\mid Y=1, A=\text{Black})< \mathbb P(\hat Y = 0\mid Y=1, A=\text{White})  \)

\(\mathbb P(Y = 0\mid \hat Y=1, A=\text{Black})\approx  \mathbb P( Y = 0\mid \hat Y=1, A=\text{White})  \)

\(\mathbb P(Y = 1\mid \hat Y=0, A=\text{Black})\approx  \mathbb P( Y = 1\mid \hat Y=0, A=\text{White})  \)

COMPAS risk predictions do not satisfy separation

Ex: Pre-trial Detention

COMPAS risk predictions do satisfy sufficiency

If we use machine learning to design a classification algorithm, how do we ensure nondiscrimination?

Achieving Nondiscrimination Criteria

Attempt #1: Remove protected attribute \(A\) from features

Attempt #2: Careful algorithmic calibration

Achieving Nondiscrimination Criteria

  • Pre-processing: remove correlations between \(A\) and features \(X\) in dataset. Requires knowledge of \(A\) during data cleaning
  • In-processing: modify learning algorithm to respect criteria.
    Requires knowledge of \(A\) at training time
  • Post-processing: adjust thresholds in group-dependent manner.
    Requires knowledge of \(A\) at decision time

Limitations of Nondiscrimination Criteria

  • Tradeoffs:  It is impossible to simultaneously satisfy separation and sufficiency if populations have different base rates
  • Observational: since they are statistical criteria, they can measure only correlation; intuitive notions of discrimination involve causation, but these can only be measured with careful modelling
  • Unclear legal grounding: anti-discrimination law looks for disparate treatment or disparate impact; while algorithmic decisions may have disparate impact, achieving criteria involves disparate treatment
  • Limited view: focusing on risk prediction might miss the bigger picture of how these tools are used by larger systems to make decisisons

Pre-existing Bias: exists independently, usually prior to the creation of the system, with roots in social institutions, practices, attitudes

Bias in Computer Systems

Technical Bias: arises from technical constraints and considerations; limitations of formalisms and quantification of the qualitative

 Emergent Bias: arises in context of use  as a result of changing societal knowledge, population, or cultural values

Classic taxonomy by Friedman & Nissenbaum (1996)

individual \(X\)

Machine Learning Pipeline

training data

\((X_i, Y_i)\)

model

\(c:\mathcal X\to \mathcal Y\)

prediction \(\hat Y\)

measurement

learning

action

Bias in Training Data

training data

\((X_i, Y_i)\)

Existing inequalities can manifest as pre-existing bias

measurement

Bias in Training Data

training data

\((X_i, Y_i)\)

Technical bias may result from the process of constructing a dataset:

  • Sample bias: who and where we sample
  • Feature/label bias: which attributes we include
  • Measurement bias: how we quantify attributes

measurement

Bias in Training Procedure

training data

\((X_i, Y_i)\)

model

\(c:\mathcal X\to \mathcal Y\)

Further technical bias results from formulating learning task and training the model: optimization bias

learning

e.g. optimizing for average accuracy will prioritize majority groups

Ex: Miscalibration in Recommendation

Designing recommendations which optimize engagement leads to over-recommending the most prevalent types (Steck, 2018)

rom-com 80% of the time

horror 20% of the time

optimize for probable click

recommend rom-com 100% of the time

Validity of Prediction Tasks

Small Is Beautiful: Economics as if People Mattered, E.F. Schumacher:

  1. Full predictability exists only in the absence of human freedom
  2. Relative predictability exists with regard to the behavior pattern of very large numbers of people doing 'normal' things
  3. Relatively full predictability exists with regard to human actions controlled by a plan which eliminates freedom
  4. Individual decisions by individuals are in principle unpredictable

Bias in Deployment

Emergent bias results from real-world dynamics, including those induced by the decisions themselves

individual \(X\)

model

\(c:\mathcal X\to \mathcal Y\)

prediction \(\hat Y\)

e.g. Goodhart's law: "When a measure becomes a target, it ceases to be a good measure."

action

Predictive policing models predict crime rate across locations based on previously recorded crimes

Ex: Emergent Bias in Policing

PredPol analyzed by Lum & Isaac (2016)

recorded drug arrests

police deployment

estimated actual drug use

Bias Beyond Classification

image cropping

facial recognition

information retrieval

generative models

Main References

  1. Barocas, Hardt, Narayanan. Fairness and machine learning: Limitations and Opportunities, Intro and Ch 1.
  2. Friedman & Nissenbaum. Bias in computer systems. TOIS, 1996.
  3. Dobbe, Dean, Gilbert, Kohli. A broader view on bias in automated decision-making: Reflecting on epistemology and dynamics. FAT/ML, 2018.

Bias in Algorithms

By Sarah Dean

Bias in Algorithms

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