ONLINE: Algorithmic Welfare. Citizen Profiling in the Public Sector

Speaker: Doris Allhutter, Austrian Academy of Sciences (ÖAW).

You are cordially invited to join a special talk of the "JKU Lecture Series Artificial Intelligence":

"Algorithmic Welfare. Citizen Profiling in the Public Sector".
Guest Lecture with Doris Allhutter (Institute of Technology Assessment | Austrian Academy of Sciences, Vienna).

Hosts: Bianca Prietl, Univ.-Prof. Dr. Uli Meyer (Department of Sociology with a focus on Innovation and Digitalization)

Date: Tuesday, 12 January 2021, 14:00-15:15

Due to the current situation, the talk is going to take place entirely online via Zoom.
Join Meeting: Link
Meeting ID: 939 6498 7674
Password: lsai

In recent years, a number of European countries have made attempts to introduce data-based decision-support systems in public job services. To make use of the ‘knowledge in data’, agencies such as the Public Employment Service Austria (AMS) have been working on the algorithmic profiling of job seekers.

Starting in 2021, a new semi-automated assistance system (short AMAS) is supposed to calculate the future chances of job seekers on Austria's labor market. Based on past statistics, job seekers will be classified into three groups, to which different resources for further education are allocated. AMAS looks for connections between job seeker characteristics and successful employment. The characteristics include age, group of countries, gender, education, care obligations and health impairments as well as past employment, contacts with the AMS and the labor market situation in the place of residence. The aim is to invest primarily in those jobseekers for whom the support measures are most likely to lead to reintegration into the labor market. The system is supposed to merely provide the AMS with an additional function in the care of jobseekers. However, the so-called AMS-algorithm has far-reaching consequences for jobseekers, AMS staff and the AMS as a public service institution.

This talk shows how the design of the AMS-algorithm is influenced by technical affordances, and most importantly by social values, norms, and interests of different stakeholders. A discussion of the tensions, challenges and biases that the system entails calls into question the objectivity and neutrality of data claims and of high hopes pinned on evidence-based decision-making. In this way, it sheds light on the coproduction of (semi)automated managerial practices in employment agencies and the framing of unemployment under the paradigmatic transformation of the welfare state to an “enabling state” that aims at mobilizing citizen’s self-responsibility.

Doris Allhutter is a senior scientist in science and technology studies at the Institute of Technology Assessment of the Austrian Academy of Sciences, Vienna (ÖAW). She researches how social inequality and difference co-emerge with sociotechnical systems and explores how practices of computing are implicitly normative and entrenched in societal power relations.

The "Lecture Series Artificial Intelligence" hosts lecturers from different scientific disciplines and backgrounds.


12.01.2021, 14:00 - 15:15


Online via Zoom


JKU, Abteilung für Soziologie mit den Schwerpunkten Innovation und Digitalisierung