• Corpus ID: 12598746

Accountable Algorithms

  title={Accountable Algorithms},
  author={Joel R. Reidenberg and Joel R. Reidenberg and David G. Robinson and Harlan YUt},
Many important decisions historically made by people are now made by computers. Algorithms count votes, approve loan and credit card applications, target citizens or neighborhoods for police scrutiny, select taxpayers for IRS audit, grant or deny immigration visas, and more. The accountability mechanisms and legal standards that govern such decision processes have not kept pace with technology. The tools currently available to policymakers, legislators, and courts were developed to oversee… 

Fair, Transparent, and Accountable Algorithmic Decision-making Processes

An overview of available technical solutions to enhance fairness, accountability, and transparency in algorithmic decision-making is provided and the Open Algortihms project is described as a step towards realizing the vision of a world where data and algorithms are used as lenses and levers in support of democracy and development.


Abstract Algorithms are now routinely used in decision-making; they are potent components in decisions that affect the lives of individuals and the activities of public and private institutions.

Blind Justice: Algorithmically Masking Race in Charging Decisions

A system that algorithmically redacts race-related information from free-text case narratives is designed to help prosecutors make race-obscured charging decisions, and highlights the promise of algorithms to bolster equitable decision-making in the criminal justice system.

Addressing Disconnection: Automated Decision-Making, Administrative Law and Regulatory Reform

  • Anna Huggins
  • Political Science
    University of New South Wales Law Journal
  • 2021
Automation is transforming how government agencies make decisions. This article analyses three distinctive features of automated decision-making that are difficult to reconcile with key doctrines of

When Are Algorithmic Decisions Perceived as Legitimate? The Effect of Process and Outcomes on Perceptions of Legitimacy of Algorithmic Decisions

Firms use algorithms to make important business decisions. To date, the algorithmic accountability literature has elided a fundamentally empirical question important to business ethics and

The Hidden Inconsistencies Introduced by Predictive Algorithms in Judicial Decision Making

This paper describes four types of inconsistencies introduced by risk prediction algorithms and considers the issue of inconsistencies due to the use of algorithms in light of current trends towards more autonomous algorithms and less human-understandable behavioral big data.

Breaking Taboos in Fair Machine Learning: An Experimental Study

Investigating attitudes toward blinding algorithms suggests that, while many respondents attest that they prefer blind algorithms, their preference is not based on an absolute principle, and in circumstances where blinding serves to disadvantage marginalized groups, respondents no longer view the exclusion of protected characteristics as a moral imperative, and the use of such information may become politically viable.

Making Artificial Intelligence Transparent: Fairness and the Problem of Proxy Variables

Combining r-transparency with ideas from the Harvard computer scientist Cynthia Dwork, this work proposes four requirements on AI systems that must be transparent when and only when regulators have an explanation, adequate for that purpose, of why it yields the predictions it does.

“Just” Algorithms: Justification (Beyond Explanation) of Automated Decisions Under the General Data Protection Regulation

If you want a sustainable environment of desirable AI systems, you should aim not only at transparent, explainable, fair, lawful, and accountable algorithms, but you also should seek for “just” algorithms, that is, automated decision-making systems that include all the above-mentioned qualities.

Algorithmic Decision-Making Based on Machine Learning from Big Data: Can Transparency Restore Accountability?

It is argued that algorithmic decisions preferably should become more understandable; to that effect, the models of machine learning to be employed should either be interpreted ex post or be interpretable by design ex ante.



Fairness-aware Learning through Regularization Approach

This paper discusses three causes of unfairness in machine learning and proposes a regularization approach that is applicable to any prediction algorithm with probabilistic discriminative models and applies it to logistic regression to empirically show its effectiveness and efficiency.

Algorithms for interpretable machine learning

This work discusses recent work on interpretable predictive modeling with decision lists and sparse integer linear models, and describes several approaches, including an algorithm based on discrete optimization, and an algorithmbased on Bayesian analysis.

The living Constitution

I. LECTURE ONE: ARE WE A NATION? The telephone rang, and a familiar conversation began: since 1989, the State Department had been badgering me to serve on delegations to advise one or another country

Learning Fair Representations

We propose a learning algorithm for fair classification that achieves both group fairness (the proportion of members in a protected group receiving positive classification is identical to the

Privacy in Pharmacogenetics: An End-to-End Case Study of Personalized Warfarin Dosing

It is concluded that current DP mechanisms do not simultaneously improve genomic privacy while retaining desirable clinical efficacy, highlighting the need for new mechanisms that should be evaluated in situ using the general methodology introduced by this work.

See Barocas & Selbst, supra note 8, at 694-714 (noting the ways in which algorithmic data mining techniques can lead to unintentional discrimination against historically prejudiced groups)

    Freedom of Information Act requires access to government held information); 15 U.S.C. § 6803 (GLBA requires financial service providers to provide annual privacy notices as a transparency measure

    • Freedom of Information Act requires access to government held information); 15 U.S.C. § 6803 (GLBA requires financial service providers to provide annual privacy notices as a transparency measure

    Congress gave the Copyright Office the power to create exemptions from the statute's prohibition on anti-circumvention

    • Congress gave the Copyright Office the power to create exemptions from the statute's prohibition on anti-circumvention

    Rule 802 (excludes evidence from hearsay) 159

    • Rule 802 (excludes evidence from hearsay) 159