Materiality and Risk in the Age of Pervasive AI Sensors
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arXiv
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| Format: | Preprint |
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2024
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| author | Sloane, Mona Moss, Emanuel Kennedy, Susan Stewart, Matthew Warden, Pete Plancher, Brian Reddi, Vijay Janapa |
| author_facet | Sloane, Mona Moss, Emanuel Kennedy, Susan Stewart, Matthew Warden, Pete Plancher, Brian Reddi, Vijay Janapa |
| contents | Artificial intelligence (AI) systems connected to sensor-laden devices are becoming pervasive, which has notable implications for a range of AI risks, including to privacy, the environment, autonomy and more. There is therefore a growing need for increased accountability around the responsible development and deployment of these technologies. Here we highlight the dimensions of risk associated with AI systems that arise from the material affordances of sensors and their underlying calculative models. We propose a sensor-sensitive framework for diagnosing these risks, complementing existing approaches such as the US National Institute of Standards and Technology AI Risk Management Framework and the European Union AI Act, and discuss its implementation. We conclude by advocating for increased attention to the materiality of algorithmic systems, and of on-device AI sensors in particular, and highlight the need for development of a sensor design paradigm that empowers users and communities and leads to a future of increased fairness, accountability and transparency. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2402_11183 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Materiality and Risk in the Age of Pervasive AI Sensors Sloane, Mona Moss, Emanuel Kennedy, Susan Stewart, Matthew Warden, Pete Plancher, Brian Reddi, Vijay Janapa Computers and Society Human-Computer Interaction Artificial intelligence (AI) systems connected to sensor-laden devices are becoming pervasive, which has notable implications for a range of AI risks, including to privacy, the environment, autonomy and more. There is therefore a growing need for increased accountability around the responsible development and deployment of these technologies. Here we highlight the dimensions of risk associated with AI systems that arise from the material affordances of sensors and their underlying calculative models. We propose a sensor-sensitive framework for diagnosing these risks, complementing existing approaches such as the US National Institute of Standards and Technology AI Risk Management Framework and the European Union AI Act, and discuss its implementation. We conclude by advocating for increased attention to the materiality of algorithmic systems, and of on-device AI sensors in particular, and highlight the need for development of a sensor design paradigm that empowers users and communities and leads to a future of increased fairness, accountability and transparency. |
| title | Materiality and Risk in the Age of Pervasive AI Sensors |
| topic | Computers and Society Human-Computer Interaction |
| url | https://arxiv.org/abs/2402.11183 |