| Participation is not a design fix for machine learning M Sloane, E Moss, O Awomolo, L Forlano Proceedings of the 2nd ACM Conference on Equity and Access in Algorithms …, 2022 | 158 | 2022 |
| AI’s social sciences deficit M Sloane, E Moss Nature Machine Intelligence 1 (8), 330-331, 2019 | 78 | 2019 |
| Inequality is the name of the game: thoughts on the emerging field of technology, ethics and social justice M Sloane Weizenbaum Conference, 9, 2019 | 54 | 2019 |
| A Silicon Valley love triangle: Hiring algorithms, pseudo-science, and the quest for auditability M Sloane, E Moss, R Chowdhury Patterns 3 (2), 2022 | 36 | 2022 |
| Tuning the space: Investigating the making of atmospheres through interior design practices M Sloane Interiors 5 (3), 297-314, 2014 | 25 | 2014 |
| On the need for mapping design inequalities M Sloane Design Issues 35 (4), 3-11, 2019 | 22 | 2019 |
| German ai start-ups and “ai ethics”: Using a social practice lens for assessing and implementing socio-technical innovation M Sloane, J Zakrzewski Proceedings of the 2022 ACM Conference on Fairness, Accountability, and …, 2022 | 20 | 2022 |
| The algorithmic auditing trap M Sloane OneZero (blog), March 17, 2021 | 18 | 2021 |
| An external stability audit framework to test the validity of personality prediction in AI hiring AK Rhea, K Markey, L D’Arinzo, H Schellmann, M Sloane, P Squires, ... Data Mining and Knowledge Discovery 36 (6), 2153-2193, 2022 | 17 | 2022 |
| AI reflections in 2019 AS Rich, C Rudin, JD MP, R Freeman, OR Wearn, H Shevlin, D Kanta, ... Nature Machine Intelligence 2 (1), 2-9, 2020 | 15 | 2020 |
| Tackling social inequalities in public lighting M Sloane, D Slater, J Entwistle Configuring Light/Staging the Social, 2016 | 15 | 2016 |
| Making artificial intelligence socially just: why the current focus on ethics is not enough M Sloane http://blogs.lse.ac.uk/politicsandpolicy/artificial-intelligence-and-society …, 2018 | 11 | 2018 |
| Resume format, linkedin URLs and other unexpected influences on AI personality prediction in hiring: results of an audit A Rhea, K Markey, L D'Arinzo, H Schellmann, M Sloane, P Squires, ... Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society, 572-587, 2022 | 10 | 2022 |
| To make AI fair, here’s what we must learn to do M Sloane Nature 605 (9), 2022 | 10* | 2022 |
| AI and Procurement-A Primer M Sloane, R Chowdhury, JC Havens, T Lazovich, L Rincon Alba | 9 | 2021 |
| Configuring light: Staging the social D Slater, M Sloane, J Entwistle | 8 | 2020 |
| Introducing contextual transparency for automated decision systems M Sloane, IR Solano-Kamaiko, J Yuan, A Dasgupta, J Stoyanovich Nature Machine Intelligence 5 (3), 187-195, 2023 | 7 | 2023 |
| The AI localism canvas S Verhulst, A Young, M Sloane Informationen zur Raumentwicklung 48 (3), 86-89, 2021 | 7 | 2021 |
| Participation-washing could be the next dangerous fad in machine learning M Sloane https://www.technologyreview.com/2020/08/25/1007589/participation-washing-ai …, 2020 | 6 | 2020 |
| Mapping design thinking resources outside of higher education—an exploratory study L Kimbell, M Sloane Design thinking in higher education: Interdisciplinary encounters, 141-164, 2020 | 6 | 2020 |
