{"id":1940,"date":"2022-09-14T23:48:27","date_gmt":"2022-09-14T23:48:27","guid":{"rendered":"https:\/\/ieeecscai.wpengine.com\/2023\/?page_id=1940"},"modified":"2023-01-19T02:14:57","modified_gmt":"2023-01-19T02:14:57","slug":"ethics","status":"publish","type":"page","link":"https:\/\/cai.ieee.org\/2023\/ethics\/","title":{"rendered":"ETHICAL & SOCIETAL IMPLICATIONS OF AI"},"content":{"rendered":"\t\t
\n\t\t\t\t\t\t
\n\t\t\t\t\t\t
\n\t\t\t\t\t
\n\t\t\t
\n\t\t\t\t\t\t
\n\t\t\t\t
\n\t\t\t\t\t

VERTICAL - SOCIETAL IMPLICATIONS OF AI<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t
\n\t\t\t\t\t\t
\n\t\t\t\t\t
\n\t\t\t
\n\t\t\t\t\t\t
\n\t\t\t\t
\n\t\t\t\t\t\t\t\t\t

SCOPE<\/strong><\/span><\/h3>

Trustworthy AI: Accuracy, explainability and interpretability, privacy, reliability, robustness, safety, and security or resilience to attacks<\/i><\/strong><\/p><\/div>

ABSTRACT<\/strong><\/span><\/h3>

To use AI in human-critical applications that could affect well-being, environment, lifestyle, etc., we need to trust the AI systems and those who develop and implement them. The National AI Initiative<\/a>\u00a0notes:<\/p><\/div>

\u201cTo be trustworthy, AI technologies must appropriately reflect characteristics such as accuracy, explainability and interpretability, privacy, reliability, robustness, safety, and security or resilience to attacks \u2013 and ensure that bias is mitigated.\u201d<\/i><\/p><\/div>

This vertical will focus on these characteristics, highlighting their technical and business challenges. The following definitions have been adapted from many sources [1]\u00a0[2]\u00a0[3].<\/p><\/div>

\u2013 Explainability and Interpretability.<\/strong>\u00a0<\/i>Understanding is a requirement for developing trust. Explanations are necessary to enhance understanding, trust, and informed decision making.\u00a0Explainability\u00a0<\/i>is the extent to which the internal mechanics of a model can be explained in human terms.\u00a0Interpretability<\/i>\u00a0is about the extent to which a cause and effect can be observed within a system.<\/p><\/div>

\u2013 Transparency.<\/i><\/strong>\u00a0The AI data, system, and business models must be transparent. Humans must be aware when they are interacting with an AI system, and must be informed of its capabilities and limitations. Ultimately, transparency improves traceability and accountability.<\/i><\/p><\/div>

\u2013 Privacy, Governance Risk, and Compliance.\u00a0<\/i><\/strong>Besides providing privacy and data protection, data governance mechanisms must also be used, to guarantee data quality and integrity, while ensuring legitimized access to data.<\/p><\/div>

\u2013 Robustness<\/i>,\u00a0<\/strong>Security\/Resilience to attacks.<\/strong>\u00a0<\/i>ML algorithms could be vulnerable to novel adversarial inputs. Trustworthy AI systems must have Robustness (the ability to withstand the effects of adversaries), including adversarial threats and attacks.<\/p><\/div>

\u2013 Fairness\/Bias.<\/i><\/strong>\u00a0Hidden biases could lead to discrimination and exclusion of underrepresented\/vulnerable groups. To prevent this, AI systems must include proper safeguards against bias and discrimination. Except for when the bias is intrinsic to the ML algorithm (Algorithm Bias<\/i>), biases are usually introduced in the ML model through its training data set:\u00a0Sample<\/i>\u00a0Bias<\/i>,\u00a0Prejudice Bias<\/i>,\u00a0Measurement Bias<\/i>, and\u00a0Exclusion Bias<\/i>.<\/p><\/div>

[1] https:\/\/www.onespan.com\/blog\/trustworthy-ai-why-we-need-it-and-how-achieve-it
[2] https:\/\/www.technologyreview.com\/2020\/03\/25\/950291\/trustworthy-ai-is-a-framework-to-help-manage-unique-risk\/
[3] https:\/\/digital-strategy.ec.europa.eu\/en\/library\/ethics-guidelines-trustworthy-ai<\/p><\/div>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t

\n\t\t\t\t\t\t
\n\t\t\t\t\t
\n\t\t\t
\n\t\t\t\t\t\t
\n\t\t\t\t
\n\t\t\t\t\t\t\t\t\t

CHALLENGES AREAS<\/strong><\/span><\/h3>
  • Bridging the principles to practice gap in data and AI ethics for business who wish to build trustworthy technology with positive social impact (AI for Good)<\/li>
  • Building public trust in technology through public engagement and co-ideation with businesses and academics \u2013 societal informed research.<\/li>
  • Accountability and Responsibility of tech.<\/li>
  • Data privacy (GDPR), data sharing in the public sector, the role of synthetic data, consent on how and why data issued, the changing face of personal data. What is trusted data sharing? (Privacy preserving data mining)<\/li>
  • Legislation \u2013 dynamic \u2013 being ahead of the curve<\/li>
  • Environmental impact v sustainability (red and green AI)<\/li><\/ul>

    COMMON TECHNOLOGY CHALLENGES<\/strong><\/span><\/h3>
    • Building public trust through explainable AI to the public \u2013 beyond algorithms and models<\/li>
    • Upskilling SMEs on doing AI ethical (bias, fairness, data representations, algorithmic transparency, interpretability etc.)<\/li>
    • Red and Green AI (Environmentally friendly and sustainable AI)<\/li>
    • Explainability V Transparency V Intellectual Property Rights<\/li>
    • What does a Code of AI Ethics and Governance look like?<\/li><\/ul>

      COMMON TECHNOLOGY CHALLENGES<\/strong><\/span><\/h3>
      • Lack of skills, resources (time and money), lack of access to real world data<\/li>
      • Ethical and responsible approach not embedded into business plan (cost effectiveness \u2013 improves reputation).<\/li>
      • Accountability dimensions: moral, social, legal, ethical, political, economic, environmental (multi-objective and fuzzy)<\/li><\/ul>

        SOLUTIONS ATTENDEES MIGHT WANT TO KNOW<\/strong><\/span><\/h3>
        • P7000 standards \u2013 accessibility<\/li>
        • The IEEE Ethics certification program.<\/li>
        • How to integrate ethics into the technical data science \/ ML pipeline.<\/li>
        • Data privacy (state of the art solutions especially with personal data), data sharing, the role of synthetic data.<\/li>
        • Current dos and don\u2019ts (i.e, GDPR) with regards to the use of data in AI model building.<\/li>
        • How to build public trust through co-ideation and co-production (i.e., citizen juries)<\/li>
        • The role of diversity and inclusion in tech ethics \u2013 making solutions more inclusive.<\/li><\/ul>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t
          \n\t\t\t\t\t\t
          \n\t\t\t\t\t
          \n\t\t\t
          \n\t\t\t\t\t\t
          \n\t\t\t\t
          \n\t\t\t\t\t\t\t\t\t

          ORGANIZING TEAM\u00a0<\/strong><\/span><\/h3>

          Keeley Crockett (Manchester Metropolitan University)
          Catherine Huang (Google)
          Rajesh Murthy (GAPASK Inc.)
          Andreas Nuernberger (Otto-von-Guericke-Universit\u00e4t Magdeburg)
          Francesco Flammini (M\u00e4lardalen University)
          Marcello Ienca (EPFL CDH-DIR)
          Alessandro Facchini (AI Labs IDSIA)
          Christian Wagner (Nottingham University)<\/p><\/div>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t

          \n\t\t\t\t\t\t
          \n\t\t\t\t\t
          \n\t\t\t
          \n\t\t\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<\/div>\n\t\t","protected":false},"excerpt":{"rendered":"

          VERTICAL – SOCIETAL IMPLICATIONS OF AI SCOPE Trustworthy AI: Accuracy, explainability and interpretability, privacy, reliability, robustness, safety, and security or resilience to attacks ABSTRACT To use AI in human-critical applications that could affect well-being, environment, lifestyle, etc., we need to trust the AI systems and those who develop and implement them. The National AI Initiative\u00a0notes: […]<\/p>\n","protected":false},"author":5,"featured_media":0,"parent":0,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"elementor_header_footer","meta":{"footnotes":"","_members_access_role":[],"_members_access_error":""},"class_list":["post-1940","page","type-page","status-publish","hentry"],"_links":{"self":[{"href":"https:\/\/cai.ieee.org\/2023\/wp-json\/wp\/v2\/pages\/1940","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/cai.ieee.org\/2023\/wp-json\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/cai.ieee.org\/2023\/wp-json\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/cai.ieee.org\/2023\/wp-json\/wp\/v2\/users\/5"}],"replies":[{"embeddable":true,"href":"https:\/\/cai.ieee.org\/2023\/wp-json\/wp\/v2\/comments?post=1940"}],"version-history":[{"count":0,"href":"https:\/\/cai.ieee.org\/2023\/wp-json\/wp\/v2\/pages\/1940\/revisions"}],"wp:attachment":[{"href":"https:\/\/cai.ieee.org\/2023\/wp-json\/wp\/v2\/media?parent=1940"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}