# A Human-Centric Perspective on Fairness and Transparency in Algorithmic Decision-Making

arXiv: https://arxiv.org/abs/2205.00033 · HTML: https://mlsanity.com/paper/2205.00033/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 22 of its 64 references that are in the map.
- First posted on arXiv 29 Apr 2022.

## Builds on (most cited first, 22 of 22)

- [A Unified Approach to Interpreting Model Predictions](https://mlsanity.com/paper/1705.07874/) (2017)
- ["Why Should I Trust You?": Explaining the Predictions of Any Classifier](https://mlsanity.com/paper/1602.04938/) (2016)
- [Equality of Opportunity in Supervised Learning](https://mlsanity.com/paper/1610.02413/) (2016)
- [A Survey on Bias and Fairness in Machine Learning](https://mlsanity.com/paper/1908.09635/) (2019)
- [Fair prediction with disparate impact: A study of bias in recidivism prediction instruments](https://mlsanity.com/paper/1610.07524/) (2016)
- [Inherent Trade-Offs in the Fair Determination of Risk Scores](https://mlsanity.com/paper/1609.05807/) (2016)
- [Avoiding Discrimination through Causal Reasoning](https://mlsanity.com/paper/1706.02744/) (2017)
- [What Do We Want From Explainable Artificial Intelligence (XAI)? -- A Stakeholder Perspective on XAI and a Conceptual Model Guiding Interdisciplinary XAI Research](https://mlsanity.com/paper/2102.07817/) (2021)
- ['It's Reducing a Human Being to a Percentage'; Perceptions of Justice in Algorithmic Decisions](https://mlsanity.com/paper/1801.10408/) (2018)
- [A Case for Humans-in-the-Loop: Decisions in the Presence of Erroneous Algorithmic Scores](https://mlsanity.com/paper/2002.08035/) (2020)
- [Mathematical Notions vs. Human Perception of Fairness: A Descriptive Approach to Fairness for Machine Learning](https://mlsanity.com/paper/1902.04783/) (2019)
- [Explainability Pitfalls: Beyond Dark Patterns in Explainable AI](https://mlsanity.com/paper/2109.12480/) (2021)
- [Explaining Models: An Empirical Study of How Explanations Impact Fairness Judgment](https://mlsanity.com/paper/1901.07694/) (2019)
- [This Thing Called Fairness: Disciplinary Confusion Realizing a Value in Technology](https://mlsanity.com/paper/1909.11869/) (2019)
- [Dark Patterns at Scale: Findings from a Crawl of 11K Shopping Websites](https://mlsanity.com/paper/1907.07032/) (2019)
- [Auditing for Discrimination in Algorithms Delivering Job Ads](https://mlsanity.com/paper/2104.04502/) (2021)
- [Deontological Ethics By Monotonicity Shape Constraints](https://mlsanity.com/paper/2001.11990/) (2020)
- [Appropriate Fairness Perceptions? On the Effectiveness of Explanations in Enabling People to Assess the Fairness of Automated Decision Systems](https://mlsanity.com/paper/2108.06500/) (2021)
- [A Study on Fairness and Trust Perceptions in Automated Decision Making](https://mlsanity.com/paper/2103.04757/) (2021)
- [The Tyranny of Data? The Bright and Dark Sides of Data-Driven Decision-Making for Social Good](https://mlsanity.com/paper/1612.00323/) (2016)
- [A Ranking Approach to Fair Classification](https://mlsanity.com/paper/2102.04565/) (2021)
- [Perceptions of Fairness and Trustworthiness Based on Explanations in Human vs. Automated Decision-Making](https://mlsanity.com/paper/2109.05792/) (2021)

## Built on by (most cited first, 1 of 1)

- [Where are the Humans? A Scoping Review of Fairness in Multi-agent AI Systems](https://mlsanity.com/paper/2604.15078/) (2026)

## Abstract (arXiv)

Automated decision systems (ADS) are increasingly used for consequential decision-making. These systems often rely on sophisticated yet opaque machine learning models, which do not allow for understanding how a given decision was arrived at. This is not only problematic from a legal perspective, but non-transparent systems are also prone to yield unfair outcomes because their sanity is challenging to assess and calibrate in the first place -- which is particularly worrisome for human decision-subjects. Based on this observation and building upon existing work, I aim to make the following three main contributions through my doctoral thesis: (a) understand how (potential) decision-subjects perceive algorithmic decisions (with varying degrees of transparency of the underlying ADS), as compared to similar decisions made by humans; (b) evaluate different tools for transparent decision-making with respect to their effectiveness in enabling people to appropriately assess the quality and fairness of ADS; and (c) develop human-understandable technical artifacts for fair automated decision-making. Over the course of the first half of my PhD program, I have already addressed substantial pieces of (a) and (c), whereas (b) will be the major focus of the second half.