Public Deliverables for the HECAT project will be added to this page
D1.3 – Report ethical, social, theological, technical review of 1st generation PES algorithms and data use
The purpose of this report is to support the development of disruptive technology, by examining incumbent technologies used in some Public Employment Services (PES), what we term first-generation algorithmic profiling tools. So, here we report on an ethical, social, theological, technical review of thirteen state of the art deployments, unpicking their general approach to offer a detailed understanding of incumbent approaches.
This document outlines the early ethical vision for the development of the HECAT platform. The objective is to produce a tool that supports the decision making of individuals and Public Employment Systems (PES) around people’s unemployment in ways that reflect European and humanitarian values of social solidarity, equity and ethics.
This document outlines a user vision statement for the development of the HECAT platform. The HECAT project’s aim is to develop a new type of algorithmic approach that integrates the qualitative and experiential aspects of unemployment; it is in other words to work with the unemployed rather than on them.
This document outlines the terms of reference for the Management boards and Steering Group of the HECAT project including meeting schedules and responsibilities.
The user context document describes the environment around unemployment services and outlines the con- siderations for social requirements when developing the platform in Slovenia. Based on document studies, statistics and interviews with counsellors and unemployed persons at the pilot deployment sites we aim to capture the user environment.
This report presents the legal considerations that one should take when using data and algorithms in public employment services. It largely draws on examples from statistical profiling as this is currently one of the most common algorithmic tools used in public employment services (PES). Importantly, the general lessons are applicable beyond profiling for example for the purpose of data-driven decision support systems in PES.