Algorithmic Fairness and Feasibility
Bibliographic Data
| ID | 19562467 |
|---|---|
| Authors | Eva Erman (0000-0001-7096-9157, Stockholm University, corresponding author), Markus Furendal (0000-0002-2378-750X, Stockholm University), Naomi Moller (0000-0003-2645-8995, Stockholm University), Niklas Möller |
| Year | 2025 |
| Volume | 38 |
| Issue | 1 |
| Publication date | 2025-03-01 |
| Peer Reviewed | Yes |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | Philosophy & Technology (JOURNAL) |
| Journal identifiers | ISSN: 2210-5433 • E-ISSN: 2210-5441 |
| Publisher | Springer Science and Business Media LLC (PUBLISHER) |
| DOI | 10.1007/s13347-024-00835-8 |
| OpenAlex | W4406107831 |
| Language | EN |
| Citations received | 1 |
| References cited | 9 |
The “impossibility results” in algorithmic fairness suggest that a predictive model cannot fully meet two common fairness criteria – sufficiency and separation – except under extraordinary circumstances. These findings have sparked a discussion on fairness in algorithms, prompting debates over whether predictive models can avoid unfair discrimination based on protected attributes, such as ethnicity or gender. As shown by Otto Sahlgren, however, the discussion of the impossibility results would gain from importing some of the tools developed in the philosophical literature on feasibility. Utilizing these tools, Sahlgren sketches a cautiously optimistic view of how algorithmic fairness can be made feasible in restricted local decision-making. While we think it is a welcome move to inject the literature on feasibility into the debate on algorithmic fairness, Sahlgren says very little about what are the general gains of bringing in feasibility considerations in theorizing algorithmic fairness. How, more precisely, does it help us make assessments about fairness in algorithmic decision-making? This is what is addressed in this Reply. More specifically, our two-fold argument is that feasibility plays an important but limited role for algorithmic fairness. We end by offering a sketch of a framework, which may be useful for theorizing feasibility in algorithmic fairness
Algorithm · Economics · Fairness measure · Impossibility · Management science · Political science · Sketch · Throughput · Computer Science · Ethics and Social Impacts of AI · Free Will and Agency · Law · Neuroethics, Human Enhancement, Biomedical Innovations
| Unique citing works | 1 |
|---|---|
| Citations per year | 1 |
| Citation span | 2025 - 2025 (1) |
| Citation velocity | recent |
| Highly cited | No |
| Citation types | Neutral: 1 |