Building Trustworthy AI Solutions: A Case for Practical Solutions for Small Businesses

Abstract

Building trustworthy artificial intelligence (AI) solutions, whether in academia or industry, must take into consideration a number of dimensions including legal, social, ethical, public opinion, and environmental aspects. A plethora of guidelines, principles, and toolkits have been published globally, but have seen limited grassroots implementation, especially among small- and medium-sized enterprises (SMEs), mainly due to the lack of knowledge, skills, and resources. In this article, we report on qualitative SME consultations over two events to establish their understanding of both data and AI ethical principles and to identify the key barriers SMEs face in their adoption of ethical AI approaches. We then use independent experts to review and code 77 published toolkits designed to build and support ethical and responsible AI practices, based on 33 evaluation criteria. The toolkits were evaluated considering their scope to address the identified SME barriers to adoption, human-centric AI principles, AI life cycle stages, and key themes around responsible AI and practical usability. Toolkits were ranked on the basis of criteria coverage and expert intercoder agreement. Results show that there is not a one-size-fits-all toolkit that addresses all criteria suitable for SMEs. Our findings show few exemplars of practical application, little guidance on how to use/apply the toolkits, and very low uptake by SMEs. Our analysis provides a mechanism for SMEs to select their own toolkits based on their current capacity, resources, and ethical awareness levels - focusing initially at the conceptualization stage of the AI life cycle and then extending throughout.