Fairness and accountability design needs for algorithmic support in high-stakes public sector decision-making
Calls for heightened consideration of fairness and accountability in algorithmically-informed public decisions—like taxation, justice, and child protection—are now commonplace. How might designers support such human values? We interviewed 27 public sector machine learning practitioners across 5 OECD...
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| Format: | Conference item |
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Association for Computing Machinery
2018
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| _version_ | 1797083433469476864 |
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| author | Veale, M Van Kleek, M Binns, R |
| author_facet | Veale, M Van Kleek, M Binns, R |
| author_sort | Veale, M |
| collection | OXFORD |
| description | Calls for heightened consideration of fairness and accountability in algorithmically-informed public decisions—like taxation, justice, and child protection—are now commonplace. How might designers support such human values? We interviewed 27 public sector machine learning practitioners across 5 OECD countries regarding challenges understanding and imbuing public values into their work. The results suggest a disconnect between organisational and institutional realities, constraints and needs, and those addressed by current research into usable, transparent and ‘discrimination-aware’ machine learning—absences likely to undermine practical initiatives unless addressed. We see design opportunities in this disconnect, such as in supporting the tracking of concept drift in secondary data sources, and in building usable transparency tools to identify risks and incorporate domain knowledge, aimed both at managers and at the ‘street-level bureaucrats’ on the frontlines of public service. We conclude by outlining ethical challenges and future directions for collaboration in these high-stakes applications. |
| first_indexed | 2024-03-07T01:41:45Z |
| format | Conference item |
| id | oxford-uuid:971b98f0-a4ae-4220-ab53-5fbea0296633 |
| institution | University of Oxford |
| last_indexed | 2024-03-07T01:41:45Z |
| publishDate | 2018 |
| publisher | Association for Computing Machinery |
| record_format | dspace |
| spelling | oxford-uuid:971b98f0-a4ae-4220-ab53-5fbea02966332022-03-26T23:57:14ZFairness and accountability design needs for algorithmic support in high-stakes public sector decision-makingConference itemhttp://purl.org/coar/resource_type/c_5794uuid:971b98f0-a4ae-4220-ab53-5fbea0296633Symplectic Elements at OxfordAssociation for Computing Machinery2018Veale, MVan Kleek, MBinns, RCalls for heightened consideration of fairness and accountability in algorithmically-informed public decisions—like taxation, justice, and child protection—are now commonplace. How might designers support such human values? We interviewed 27 public sector machine learning practitioners across 5 OECD countries regarding challenges understanding and imbuing public values into their work. The results suggest a disconnect between organisational and institutional realities, constraints and needs, and those addressed by current research into usable, transparent and ‘discrimination-aware’ machine learning—absences likely to undermine practical initiatives unless addressed. We see design opportunities in this disconnect, such as in supporting the tracking of concept drift in secondary data sources, and in building usable transparency tools to identify risks and incorporate domain knowledge, aimed both at managers and at the ‘street-level bureaucrats’ on the frontlines of public service. We conclude by outlining ethical challenges and future directions for collaboration in these high-stakes applications. |
| spellingShingle | Veale, M Van Kleek, M Binns, R Fairness and accountability design needs for algorithmic support in high-stakes public sector decision-making |
| title | Fairness and accountability design needs for algorithmic support in high-stakes public sector decision-making |
| title_full | Fairness and accountability design needs for algorithmic support in high-stakes public sector decision-making |
| title_fullStr | Fairness and accountability design needs for algorithmic support in high-stakes public sector decision-making |
| title_full_unstemmed | Fairness and accountability design needs for algorithmic support in high-stakes public sector decision-making |
| title_short | Fairness and accountability design needs for algorithmic support in high-stakes public sector decision-making |
| title_sort | fairness and accountability design needs for algorithmic support in high stakes public sector decision making |
| work_keys_str_mv | AT vealem fairnessandaccountabilitydesignneedsforalgorithmicsupportinhighstakespublicsectordecisionmaking AT vankleekm fairnessandaccountabilitydesignneedsforalgorithmicsupportinhighstakespublicsectordecisionmaking AT binnsr fairnessandaccountabilitydesignneedsforalgorithmicsupportinhighstakespublicsectordecisionmaking |