The limits of privacy in automated profiling and data mining

Schermer, Bart

doi:10.1016/j.clsr.2010.11.009

Cited by 116 publications

(72 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…This much is implicit in Schermer's (2011) argument that discriminatory treatment is not ethically problematic in itself; rather, it is the effects of the treatment that determine its ethical acceptability. However, Schermer muddles bias and discrimination into a single concept.…”

Section: Unfair Outcomes Leading To Discriminationmentioning

confidence: 99%

“…The algorithm 'learns' by defining rules to determine how new inputs will be classified. The model can be taught to the algorithm via hand labelled inputs (supervised learning); in other cases the algorithm itself defines best-fit models to make sense of a set of inputs (unsupervised learning) 5 (Schermer, 2011;Van Otterlo, 2013). In both cases, the algorithm defines decision-making rules to handle new inputs.…”

Section: Introductionmentioning

confidence: 99%

“…Rubel and Jones (2014) argue that the failure to render the processing logic comprehensible to data subject's disrespects their agency (we shall return to this point in Section 8). Meaningful consent to dataprocessing is not possible when opacity precludes risk assessment (Schermer, 2011). Releasing information about the algorithm's decision-making logic in a simplified format can help (Datta et al, 2016;Tene and Polonetsky, 2013a).…”

mentioning

confidence: 99%

“…Burrell (2016) and Schermer (2011) argue that the opacity of machine learning algorithms inhibits oversight. Algorithms ''are opaque in the sense that if one is a recipient of the output of the algorithm (the classification decision), rarely does one have any concrete sense of how or why a particular classification has been arrived at from inputs'' (Burrell, 2016: 1).…”

mentioning

confidence: 99%

See 3 more Smart Citations

The ethics of algorithms: Mapping the debate

et al. 2016

View full text Add to dashboard Cite

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 design and operation of algorithms and our understanding of their ethical implications can have severe consequences affecting individuals as well as groups and whole societies. This paper makes three contributions to clarify the ethical importance of algorithmic mediation. It provides a prescriptive map to organise the debate. It reviews the current discussion of ethical aspects of algorithms. And it assesses the available literature in order to identify areas requiring further work to develop the ethics of algorithms.

show abstract

Section: Unfair Outcomes Leading To Discriminationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

mentioning

confidence: 99%

mentioning

confidence: 99%

See 2 more Smart Citations

The ethics of algorithms: Mapping the debate

et al. 2016

View full text Add to dashboard Cite

show abstract

“…Protecting the informational privacy of H-IoT users is key, as privacy empowers users to control data about themselves, limiting opportunities for unwanted disturbances and exploitation [33][34][35]. Information enables regulation, behavioural control, and profiling by those with greater access [36].…”

Section: Respect Individual Privacymentioning

confidence: 99%

Designing the Health-related Internet of Things: Ethical Principles and Guidelines

Mittelstadt

2017

Information

View full text Add to dashboard Cite

Abstract:The conjunction of wireless computing, ubiquitous Internet access, and the miniaturisation of sensors have opened the door for technological applications that can monitor health and well-being outside of formal healthcare systems. The health-related Internet of Things (H-IoT) increasingly plays a key role in health management by providing real-time tele-monitoring of patients, testing of treatments, actuation of medical devices, and fitness and well-being monitoring. Given its numerous applications and proposed benefits, adoption by medical and social care institutions and consumers may be rapid. However, a host of ethical concerns are also raised that must be addressed. The inherent sensitivity of health-related data being generated and latent risks of Internet-enabled devices pose serious challenges. Users, already in a vulnerable position as patients, face a seemingly impossible task to retain control over their data due to the scale, scope and complexity of systems that create, aggregate, and analyse personal health data. In response, the H-IoT must be designed to be technologically robust and scientifically reliable, while also remaining ethically responsible, trustworthy, and respectful of user rights and interests. To assist developers of the H-IoT, this paper describes nine principles and nine guidelines for ethical design of H-IoT devices and data protocols.

show abstract

GeoAI and Its Implications

Sieber

2022

International Encyclopedia of Geography

View full text Add to dashboard Cite

Artificial Intelligence (AI) has emerged as a major analytical method in geography, aided by advancements in computational power, software architectures, sensing hardware, and the emergence of big data and volunteered geographic information. This entry briefly covers the history of AI in geography. This includes potential applications of geospatial AI or GeoAI, such as predictive modeling for health and neighborhoods, transportation monitoring, land use, and cartography. The inclusion of AI in geographic methods must engage with ongoing debates on opacity and transferability, algorithmic bias, geosurveillance and locational privacy, as well as the need for AI ethics.

show abstract

The limits of privacy in automated profiling and data mining

Cited by 116 publications

References 0 publications

The ethics of algorithms: Mapping the debate

The ethics of algorithms: Mapping the debate

Designing the Health-related Internet of Things: Ethical Principles and Guidelines

GeoAI and Its Implications

Contact Info

Product

Resources

About