Data Mining and Automated Discrimination: A Mixed Legal/Technical Perspective

Carmichael, Laura; Stalla-Bourdillon, Sophie; Staab, Steffen

doi:10.1109/mis.2016.96

Cited by 21 publications

(9 citation statements)

References 8 publications

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“…Everyone should get a chance to succeed, we should judge every person based on his/her individuality, not based on their gender, race, ethnicity, etc. This is an explanation many people give; see, e.g., [1].…”

Section: Is It Legitimate Statistics or Is It Sexism?mentioning

confidence: 87%

Is It Legitimate Statistics or Is It Sexism: Why Discrimination Is Not Rational

Escobar

Kreinovich

Nguyen

2017

Econometrics for Financial Applications

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While in the ideal world, everyone should have the same chance to succeed in a given profession, in reality, often the probability of success is different for people of different gender and/or ethnicity. For example, in the US, the probability of a female undergraduate student in computer science to get a PhD is lower than a similar probability for a male student. At first glance, it may seem that in such a situation, if we try to maximize our gain and we have a limited amount of resources, it is reasonable to concentrate on students with the higher probability of successi.e., on males, and only moral considerations prevent us from pursuing this seemingly economically optimal discriminatory strategy. In this paper, we show that this first impression is wrong: the discriminatory strategy is not only morally wrong, it is also not optimal-and the morally preferable inclusive strategy is actually also economically better.

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Section: Is It Legitimate Statistics or Is It Sexism?mentioning

confidence: 87%

Is It Legitimate Statistics or Is It Sexism: Why Discrimination Is Not Rational

Escobar

Kreinovich

Nguyen

2017

Econometrics for Financial Applications

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“…A critical system design choice is how aggregation is performed, which party performs the aggregation, and what the implications of the aggregation design are for the citizens. On the one hand, collecting individual citizen data to perform a centralized aggregation at the site of the service provider requires the reveal of personal data and opens up opportunities for discriminatory data analytics [31,115]. For instance, utility companies can perform energy disaggregation to infer with high accuracy the lifestyle and residential activities of citizens [55].…”

Section: Challenges and Opportunities In Data Gathering: Distributed mentioning

confidence: 99%

(So) Big Data and the transformation of the city

Andrienko

Boldrini

Caldarelli

et al. 2020

Int J Data Sci Anal

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The exponential increase in the availability of large-scale mobility data has fueled the vision of smart cities that will transform our lives. The truth is that we have just scratched the surface of the research challenges that should be tackled in order to make this vision a reality. Consequently, there is an increasing interest among different research communities (ranging from civil engineering to computer science) and industrial stakeholders in building knowledge discovery pipelines over such data sources. At the same time, this widespread data availability also raises privacy issues that must be considered by both industrial and academic stakeholders. In this paper, we provide a wide perspective on the role that big data have in reshaping cities. The paper covers the main aspects of urban data analytics, focusing on privacy issues, algorithms, applications and services, and georeferenced data from social media. In discussing these aspects, we leverage, as concrete examples and case studies of urban data science tools, the results obtained in the "City of Citizens" thematic area of the Horizon 2020 SoBigData initiative, which includes a virtual research environment with mobility datasets and urban analytics methods developed by several institutions around Europe. We conclude the paper outlining the main research challenges that urban data science has yet to address in order to help make the smart city vision a reality.

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“…The academic community has been cognizant of these issues from the outset of emergence of the concept of knowledge discovery and technologies of data mining by proactively studying a variety of countermeasures including such approaches as adversarial learning or a suite of diverse privacy mechanisms, among others. One can point here to interesting studies and review materials on privacy preserving in data mining (Cuzzocrea, ) (Wang, Luo, Zhao, & Le, ), security (Xu, Jiang, Wang, Yuan, & Ren, ), legal aspects (Carmichael, Stalla‐Bourdillon, & Staab, ), and general considerations on ethics and technology (Munoz, ).…”

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confidence: 99%

The benefits and drawbacks of data mining technologies

Pedrycz¹

2019

WIREs Data Min & Knowl

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Data Mining and Automated Discrimination: A Mixed Legal/Technical Perspective

Cited by 21 publications

References 8 publications

Is It Legitimate Statistics or Is It Sexism: Why Discrimination Is Not Rational

Is It Legitimate Statistics or Is It Sexism: Why Discrimination Is Not Rational

(So) Big Data and the transformation of the city

The benefits and drawbacks of data mining technologies

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