Algorithmic bias challenges in agency decisions
What is Algorithmic Bias in Agency Decisions?
Algorithmic bias refers to systematic and unfair discrimination embedded in automated decision-making systems or algorithms used by government agencies. These biases can arise from:
Biased training data that reflect historical inequalities
Poorly designed algorithms that unintentionally perpetuate stereotypes or unequal treatment
Lack of transparency and accountability in how algorithms make decisions
When agencies use algorithms to make decisions (e.g., in welfare, criminal justice, immigration), biased outcomes can violate principles of fairness, equal protection, and due process.
Why is Algorithmic Bias a Challenge in Agency Decisions?
Opacity: Algorithms are often “black boxes,” making it hard to understand how decisions are made.
Discrimination: Algorithmic decisions can disproportionately harm minorities or vulnerable groups.
Due Process: Automated decisions may lack meaningful human oversight.
Accountability: It’s unclear who is responsible when an algorithm harms someone.
Case Law Examples Addressing Algorithmic Bias in Agency Decisions
1. State v. Loomis, 2016 (Wisconsin Supreme Court)
Context: This case involved the use of the COMPAS algorithm in sentencing decisions. COMPAS predicts a defendant’s risk of recidivism.
Issue: The defendant argued that COMPAS was biased and violated his due process rights because the algorithm’s workings were secret and it disproportionately scored African American defendants as higher risk.
Ruling: The court allowed the use of COMPAS but cautioned judges to not rely solely on it. The court highlighted concerns about transparency and potential bias but stopped short of banning the tool.
Significance: This was one of the first major cases to recognize algorithmic bias concerns in criminal justice. It underscored the need for human judgment alongside algorithmic recommendations.
2. EPIC v. DOJ (2019) - Freedom of Information Act (FOIA) Case
Context: The Electronic Privacy Information Center (EPIC) sued the Department of Justice to disclose records about the use of predictive policing algorithms.
Issue: EPIC argued that secretive use of algorithms in law enforcement violated transparency norms and could perpetuate racial biases in policing decisions.
Outcome: The court ordered partial disclosure but also recognized government’s interest in protecting some information. The case pushed agencies toward greater transparency about algorithmic tools.
Significance: Though not a traditional adjudication of bias, this case highlighted the challenge of agency secrecy surrounding algorithms and raised public awareness of bias risks.
3. U.S. v. Microsoft (2021) - Facial Recognition Technology
Context: Microsoft’s facial recognition software was used by some agencies for identity verification.
Issue: The software was found to have higher error rates for darker-skinned individuals and women. Lawsuits alleged this biased technology violated equal protection under agency decision contexts.
Ruling: While not a criminal or administrative case, settlements and regulatory pressure forced Microsoft to improve fairness and accuracy, limiting agency use until biases were addressed.
Significance: This case reflects broader agency concerns about biased biometrics and the legal pressures to mitigate them before deployment.
4. Tennessee v. Lane (2004)
Context: Though not about algorithms per se, this Supreme Court case addressed due process and access rights in agency decisions, which are central to debates on automated decision-making fairness.
Issue: Tennessee’s court system failed to provide accessible services for disabled individuals. The case established that agencies must provide meaningful access and due process.
Application to Algorithms: This ruling has been cited to argue that agencies must ensure algorithmic decisions do not deny fundamental rights and access to justice, emphasizing procedural fairness.
5. New York City Stop-and-Frisk Litigation (Floyd v. City of New York, 2013)
Context: The NYPD’s Stop-and-Frisk policy disproportionately targeted minorities, and the city later introduced predictive policing algorithms.
Issue: The court ruled the Stop-and-Frisk policy unconstitutional due to racial bias, raising concerns about predictive algorithms perpetuating similar discrimination.
Outcome: Following the ruling, courts and advocacy groups have closely scrutinized algorithmic policing tools for bias.
Significance: This case shows how bias in human decision-making can be mirrored or exacerbated by algorithmic systems, influencing agency reform.
Summary: Key Themes from These Cases
Transparency & Accountability: Courts want agencies to be clear about how algorithms work and hold them accountable.
Human Oversight: Algorithms can assist but cannot replace human judgment.
Equal Protection: Agencies must ensure algorithmic decisions do not violate constitutional protections against discrimination.
Access & Fairness: Agencies must maintain fair procedures, especially when automation impacts fundamental rights.
RELATED Blog
0 comments