Sandra Wachter
Biographic Data
| ID | 297956 |
|---|---|
| NAME | Sandra Wachter |
| GIVEN NAMES | Sandra |
| FAMILY NAME | Wachter |
| SIGNATURE | WACHTER S |
| AFFILIATIONS | University of Oxford |
| ORCID | 0000-0003-3800-0113 |
| VERIFIED | Yes |
| TOTAL WORKS | 9 |
| TOTAL CITATIONS | 145 |
| AUTHOR COUNT | 9 |
| EDITOR COUNT | 0 |
| FIRST PUBLICATION YEAR | 2016 |
| LATEST PUBLICATION YEAR | 2024 |
| H-INDEX | 4 |
Beyond ideals: Why the (medical) AI industry needs to motivate behavioural change in line with fairness and transparency values, and how it can do it
Artificial intelligence (AI) is increasingly relied upon by clinicians for making diagnostic and treatment decisions, playing an important role in imaging, diagnosis, risk analysis, lifestyle monitoring, and health information management. While research has identified biases in healthcare AI systems and proposed technical solutions to address these, we argue that effective solutions require human engagement. Furthermore, there is a lack of resear…
Trustworthy artificial intelligence and the European Union AI act: On the conflation of trustworthiness and acceptability of risk
In its AI Act, the European Union chose to understand trustworthiness of AI in terms of the acceptability of its risks. Based on a narrative systematic literature review on institutional trust and AI in the public sector, this article argues that the EU adopted a simplistic conceptualization of trust and is overselling its regulatory ambition. The paper begins by reconstructing the conflation of “trustworthiness” with “acceptability” in the AI Ac…
To protect science, we must use LLMs as zero-shot translators
The Concentration-after-Personalisation Index (Capi): Governing effects of personalisation using the example of targeted online advertising
Firms are increasingly personalising their offers and services, leading to an ever finer-grained segmentation of consumers online. Targeted online advertising and online price discrimination are salient examples of this development. While personalisation's overall effects on consumer welfare are expectably ambiguous, it can lead to concentration in the distribution of advertising and commercial offers. Constellations are possible in which a marke…
Explaining Explanations in AI
Recent work on interpretability in machine learning and AI has focused on the building of simplified models that approximate the true criteria used to make decisions. These models are a useful pedagogical device for teaching trained professionals how to predict what decisions will be made by the complex system, and most importantly how the system might break. However, when considering any such model it's important to remember Box's maxim that "Al…
A Right to Reasonable Inferences: Re-Thinking Data Protection Law in the Age of Big Data and AI
Big Data analytics and artificial intelligence (AI) draw non-intuitive and unverifiable inferences and predictions about the behaviors, preferences, and private lives of individuals. These inferences draw on highly diverse and feature-rich data of unpredictable value, and create new opportunities for discriminatory, biased, and invasive decision-making. Concerns about algorithmic accountability are often actually concerns about the way in which t…
Why a Right to Explanation of Automated Decision-Making Does Not Exist in the General Data Protection Regulation
In recent months, researchers,1 government bodies,2 and the media3 have claimed that a ‘right to explanation’ of decisions made by automated and artificially intelligent algorithmic systems is legally mandated by the forthcoming European Union General Data Protection Regulation4 2016/679 (GDPR). The right to explanation is viewed as a promising mechanism in the broader pursuit by government and industry for accountability and transparency in algo…
Counterfactual Explanations Without Opening the Black Box: Automated Decisions and the GDPR
There has been much discussion of the "right to explanation" in the EU General Data Protection Regulation, and its existence, merits, and disadvantages. Implementing a right to explanation that opens the 'black box' of algorithmic decision-making faces major legal and technical barriers. Explaining the functionality of complex algorithmic decision-making systems and their rationale in specific cases is a technically challenging problem. Some expl…
The ethics of algorithms: Mapping the debate
In information societies, operations, decisions and choices previously left to humans are increasingly delegated to algorithms, which may advise, if not decide, about how data should be interpreted and what actions should be taken as a result. More and more often, algorithms mediate social processes, business transactions, governmental decisions, and how we perceive, understand, and interact among ourselves and with the environment. Gaps between …
The ethics of algorithms: Mapping the debate
In information societies, operations, decisions and choices previously left to humans are increasingly delegated to algorithms, which may advise, if not decide, about how data should be interpreted and what actions should be taken as a result. More and more often, algorithms mediate social processes, business transactions, governmental decisions, and how we perceive, understand, and interact among ourselves and with the environment. Gaps between …
Trustworthy artificial intelligence and the European Union AI act: On the conflation of trustworthiness and acceptability of risk
In its AI Act, the European Union chose to understand trustworthiness of AI in terms of the acceptability of its risks. Based on a narrative systematic literature review on institutional trust and AI in the public sector, this article argues that the EU adopted a simplistic conceptualization of trust and is overselling its regulatory ambition. The paper begins by reconstructing the conflation of “trustworthiness” with “acceptability” in the AI Ac…
Counterfactual Explanations Without Opening the Black Box: Automated Decisions and the GDPR
There has been much discussion of the "right to explanation" in the EU General Data Protection Regulation, and its existence, merits, and disadvantages. Implementing a right to explanation that opens the 'black box' of algorithmic decision-making faces major legal and technical barriers. Explaining the functionality of complex algorithmic decision-making systems and their rationale in specific cases is a technically challenging problem. Some expl…
To protect science, we must use LLMs as zero-shot translators
The Concentration-after-Personalisation Index (Capi): Governing effects of personalisation using the example of targeted online advertising
Firms are increasingly personalising their offers and services, leading to an ever finer-grained segmentation of consumers online. Targeted online advertising and online price discrimination are salient examples of this development. While personalisation's overall effects on consumer welfare are expectably ambiguous, it can lead to concentration in the distribution of advertising and commercial offers. Constellations are possible in which a marke…
The ethics of algorithms: Mapping the debate
In information societies, operations, decisions and choices previously left to humans are increasingly delegated to algorithms, which may advise, if not decide, about how data should be interpreted and what actions should be taken as a result. More and more often, algorithms mediate social processes, business transactions, governmental decisions, and how we perceive, understand, and interact among ourselves and with the environment. Gaps between …
Why a Right to Explanation of Automated Decision-Making Does Not Exist in the General Data Protection Regulation
In recent months, researchers,1 government bodies,2 and the media3 have claimed that a ‘right to explanation’ of decisions made by automated and artificially intelligent algorithmic systems is legally mandated by the forthcoming European Union General Data Protection Regulation4 2016/679 (GDPR). The right to explanation is viewed as a promising mechanism in the broader pursuit by government and industry for accountability and transparency in algo…
Counterfactual Explanations Without Opening the Black Box: Automated Decisions and the GDPR
There has been much discussion of the "right to explanation" in the EU General Data Protection Regulation, and its existence, merits, and disadvantages. Implementing a right to explanation that opens the 'black box' of algorithmic decision-making faces major legal and technical barriers. Explaining the functionality of complex algorithmic decision-making systems and their rationale in specific cases is a technically challenging problem. Some expl…
A Right to Reasonable Inferences: Re-Thinking Data Protection Law in the Age of Big Data and AI
Big Data analytics and artificial intelligence (AI) draw non-intuitive and unverifiable inferences and predictions about the behaviors, preferences, and private lives of individuals. These inferences draw on highly diverse and feature-rich data of unpredictable value, and create new opportunities for discriminatory, biased, and invasive decision-making. Concerns about algorithmic accountability are often actually concerns about the way in which t…
Explaining Explanations in AI
Recent work on interpretability in machine learning and AI has focused on the building of simplified models that approximate the true criteria used to make decisions. These models are a useful pedagogical device for teaching trained professionals how to predict what decisions will be made by the complex system, and most importantly how the system might break. However, when considering any such model it's important to remember Box's maxim that "Al…
The Concentration-after-Personalisation Index (Capi): Governing effects of personalisation using the example of targeted online advertising
Firms are increasingly personalising their offers and services, leading to an ever finer-grained segmentation of consumers online. Targeted online advertising and online price discrimination are salient examples of this development. While personalisation's overall effects on consumer welfare are expectably ambiguous, it can lead to concentration in the distribution of advertising and commercial offers. Constellations are possible in which a marke…
Trustworthy artificial intelligence and the European Union AI act: On the conflation of trustworthiness and acceptability of risk
In its AI Act, the European Union chose to understand trustworthiness of AI in terms of the acceptability of its risks. Based on a narrative systematic literature review on institutional trust and AI in the public sector, this article argues that the EU adopted a simplistic conceptualization of trust and is overselling its regulatory ambition. The paper begins by reconstructing the conflation of “trustworthiness” with “acceptability” in the AI Ac…
To protect science, we must use LLMs as zero-shot translators
Beyond ideals: Why the (medical) AI industry needs to motivate behavioural change in line with fairness and transparency values, and how it can do it
Artificial intelligence (AI) is increasingly relied upon by clinicians for making diagnostic and treatment decisions, playing an important role in imaging, diagnosis, risk analysis, lifestyle monitoring, and health information management. While research has identified biases in healthcare AI systems and proposed technical solutions to address these, we argue that effective solutions require human engagement. Furthermore, there is a lack of resear…
Computer Science (7 works) · Business (5 works) · Psychology (5 works) · Ethics and Social Impacts of AI (4 works) · Political science (4 works) · Digitalization, Law, and Regulation (3 works) · Economics (3 works) · Internet privacy (3 works) · Law (3 works) · Privacy, Security, and Data Protection (3 works)