{"id":2898,"date":"2020-12-14T09:40:00","date_gmt":"2020-12-14T09:40:00","guid":{"rendered":"https:\/\/techpolicy.org.il\/?p=2898"},"modified":"2021-09-22T09:20:11","modified_gmt":"2021-09-22T09:20:11","slug":"artificial-intelligence-in-government-implementing-algorithmic-decision-making-systems-in-welfare-services-abstract","status":"publish","type":"post","link":"https:\/\/techpolicy.org.il\/he\/blog\/artificial-intelligence-in-government-implementing-algorithmic-decision-making-systems-in-welfare-services-abstract\/","title":{"rendered":"Artificial Intelligence in Government: Implementing Algorithmic Decision-Making Systems in Welfare Services \u2013 ABSTRACT"},"content":{"rendered":"<figure class=\"wp-block-image size-large\"><img loading=\"lazy\" decoding=\"async\" width=\"760\" height=\"368\" src=\"https:\/\/techpolicy.org.il\/wp-content\/uploads\/2020\/12\/aigovernment.png\" alt=\"\" class=\"wp-image-2806\" srcset=\"https:\/\/techpolicy.org.il\/wp-content\/uploads\/2020\/12\/aigovernment.png 760w, https:\/\/techpolicy.org.il\/wp-content\/uploads\/2020\/12\/aigovernment-300x145.png 300w, https:\/\/techpolicy.org.il\/wp-content\/uploads\/2020\/12\/aigovernment-16x8.png 16w\" sizes=\"auto, (max-width: 760px) 100vw, 760px\" \/><\/figure>\n\n\n\n<p>This <a href=\"https:\/\/techpolicy.org.il\/wp-content\/uploads\/2020\/12\/DFV-%D7%91%D7%99%D7%A0%D7%94-%D7%9E%D7%9C%D7%90%D7%9B%D7%95%D7%AA%D7%99%D7%AA-%D7%91%D7%A9%D7%99%D7%A8%D7%95%D7%AA%D7%99-%D7%9E%D7%9E%D7%A9%D7%9C-%D7%94%D7%98%D7%9E%D7%A2%D7%AA-%D7%9E%D7%A2%D7%A8%D7%9B%D7%95%D7%AA-%D7%9C%D7%A7%D7%91%D7%9C%D7%AA-%D7%94%D7%97%D7%9C%D7%98%D7%95%D7%AA-%D7%9E%D7%91%D7%95%D7%A1%D7%A1%D7%95%D7%AA-%D7%90%D7%9C%D7%92%D7%95%D7%A8%D7%99%D7%AA%D7%9D-%D7%91%D7%A9%D7%99%D7%A8%D7%95%D7%AA%D7%99-%D7%94%D7%A8%D7%95%D7%95%D7%97%D7%94.pdf\" data-type=\"URL\" data-id=\"https:\/\/techpolicy.org.il\/wp-content\/uploads\/2020\/12\/DFV-%D7%91%D7%99%D7%A0%D7%94-%D7%9E%D7%9C%D7%90%D7%9B%D7%95%D7%AA%D7%99%D7%AA-%D7%91%D7%A9%D7%99%D7%A8%D7%95%D7%AA%D7%99-%D7%9E%D7%9E%D7%A9%D7%9C-%D7%94%D7%98%D7%9E%D7%A2%D7%AA-%D7%9E%D7%A2%D7%A8%D7%9B%D7%95%D7%AA-%D7%9C%D7%A7%D7%91%D7%9C%D7%AA-%D7%94%D7%97%D7%9C%D7%98%D7%95%D7%AA-%D7%9E%D7%91%D7%95%D7%A1%D7%A1%D7%95%D7%AA-%D7%90%D7%9C%D7%92%D7%95%D7%A8%D7%99%D7%AA%D7%9D-%D7%91%D7%A9%D7%99%D7%A8%D7%95%D7%AA%D7%99-%D7%94%D7%A8%D7%95%D7%95%D7%97%D7%94.pdf\" target=\"_blank\" rel=\"noreferrer noopener\">review<\/a> (in Hebrew) looks into the emerging trend of integrating AI-based technologies into governmental services; social welfare services, in particular. Our focus here is algorithmic decision-making (ADM) systems, designed to support humans in the decision-making process, or utterly replace them.<\/p>\n\n\n\n<p>ADM systems are typically applied in two major areas of welfare services: 1) \u200b\u200bwelfare benefits (determining entitlement to benefits, and identifying welfare fraud); and 2) child protection services and protection of at-risk populations (flagging child abuse cases identifying families for the purpose of early intervention; and preventing the exploitation of underprivileged populations and their deterioration into crime). This growing trend is dubbed \u2013 the &#8216;<strong>digital welfare state.<\/strong>&#8216;<\/p>\n\n\n\n<p>This work cites the particular (technological, legal, ethical, social and other) challenges and existing barriers to this integration of smart technologies into a rather conservative and technology-sceptic public service. It further provides a more in-depth analysis of potential benefits alongside difficulties, and various ethical and legal implications for implementing ADM systems in the social welfare field.<\/p>\n\n\n\n<p>Potential benefits include: increased efficiency in the provision of (public) welfare services; the employment of ADM systems as a reflection of governmental responsibility; objectivity and neutrality in decision-making, and the consequent promotion of public trust in digital services and government; the personalisation of welfare services; prevention and mitigation of future harms by flagging risk and allowing for early detection of individuals at-risk; the instrumental value of ADM systems for the promotion of social justice, the alleviation of workload for social workers and their empowerment; and more.<\/p>\n\n\n\n<p>Foreseen ethical and legal difficulties associated with the implementation of ADM systems in welfare services, consist of: <em>a)<\/em> the potential compromising of a host of personal and citizen rights including <em>inter alia<\/em>, the right to fairness, the right to justice and (technological) due process, the right to equality&nbsp;and non-discrimination, the right to autonomy and the right to privacy; and <em>b)<\/em> ethical difficulties inherent to the characteristics of ADM systems, such as non-explainability and &#8216;black box&#8217; problem; lacking responsibility and accountability for ADM systems&#8217; welfare-related decisions; decision bias and algorithmic discrimination; and more. These ethical and legal difficulties are analysed against the background of the framework of principles for human(ity)-safe AI applications, being presently formulated by international entities, local governments, and corporations (i.e., self-regulation).<strong><u><\/u><\/strong><\/p>\n\n\n\n<p>The conclusion of the review offers a set of (ADM systems&#8217;) developmental stage-related recommendations, acknowledging the inevitability of AI-based technologies&#8217; integration into governmental services, alongside the necessity of precautionary steps for their human-friendly application.<\/p>","protected":false},"excerpt":{"rendered":"<p>This review (in Hebrew) looks into the emerging trend of integrating AI-based technologies into governmental services; social welfare services, in particular. Our focus here is algorithmic decision-making (ADM) systems, designed [&hellip;]<\/p>\n","protected":false},"author":15,"featured_media":2806,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"_seopress_robots_primary_cat":"none","_seopress_titles_title":"","_seopress_titles_desc":"","_seopress_robots_index":"","inline_featured_image":false,"footnotes":""},"categories":[9],"tags":[41,42,45,46,44,43],"publication_type":[53],"class_list":["post-2898","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-blog","tag-ai","tag-ai-ethics","tag-ai-in-government","tag-data-driven-technologies","tag-data-ethics","tag-digital-welfare","publication_type-white-paper"],"acf":[],"_links":{"self":[{"href":"https:\/\/techpolicy.org.il\/he\/wp-json\/wp\/v2\/posts\/2898","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/techpolicy.org.il\/he\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/techpolicy.org.il\/he\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/techpolicy.org.il\/he\/wp-json\/wp\/v2\/users\/15"}],"replies":[{"embeddable":true,"href":"https:\/\/techpolicy.org.il\/he\/wp-json\/wp\/v2\/comments?post=2898"}],"version-history":[{"count":2,"href":"https:\/\/techpolicy.org.il\/he\/wp-json\/wp\/v2\/posts\/2898\/revisions"}],"predecessor-version":[{"id":2902,"href":"https:\/\/techpolicy.org.il\/he\/wp-json\/wp\/v2\/posts\/2898\/revisions\/2902"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/techpolicy.org.il\/he\/wp-json\/wp\/v2\/media\/2806"}],"wp:attachment":[{"href":"https:\/\/techpolicy.org.il\/he\/wp-json\/wp\/v2\/media?parent=2898"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/techpolicy.org.il\/he\/wp-json\/wp\/v2\/categories?post=2898"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/techpolicy.org.il\/he\/wp-json\/wp\/v2\/tags?post=2898"},{"taxonomy":"publication_type","embeddable":true,"href":"https:\/\/techpolicy.org.il\/he\/wp-json\/wp\/v2\/publication_type?post=2898"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}