"AI systems should help and empower society, combining the best of technology with the best of humanity.”
...but poses ethical challenges for society, individuals, organisations. In the new data-drive era, every organization needs to embrace AI. Status quo is not an alternative.
We are creating a world-leading center for identifying, measuring and governing ethical implications of AI. The AI Sustainability Center is a collaborative, research-focused environment for piloting and testing Al sustainability strategies and frameworks. The center intends to focus on mitigating risks as well as facilitating the realization
of the vast gains to organizations, society and individuals by acting proactively. It will provide an opportunity for members not only to avoid future pitfalls, but also to gain a competitive edge by learning to use Al in a sustainable way.
Industry professionals, researchers, policymakers and regulators, and
individuals need to be aware of the ethical and social implications of Al. Risks such as privacy intrusion, discrimination, and the proliferation of fake news can result in negative consequences
– intended or unintended – if Al is not governed in a sustainable way.
Unintended ethical breaches are often a result of algorithms using historical data that incorporates bias. Programmers who often lack proper skills and knowledge to understand AI’s broader societal implications can create intended or unintended values. Breaches can also result from immature Al, for example, if algorithms use a training data set which is insufficient or not diverse enough.
Organizations and individuals may rely on being GDPR compliant. But as technology is ahead of regulation, this could lead to a false sense of security. There is a regulatory blind spot.
The AI application/solution could be overly intrusive (using too broad or too deep open data) or it could be used for unintended purposes by others.
Values and bias are intentionally or unintentionally programmed by the creator who may also lack knowledge and skills of how the solution could scale in a broader context.
Insufficient training of algorithms on data sets as well as lack of representative data could lead to incorrect or inappropriate recommendations.
The data available is not an accurate reflection of reality or the preferred reality and may lead to incorrect and unethical recommendations.
• How far can we go in our collection of personal information for credit risk scoring?
• Would you want your algorithms to pick or support your next decision about your CEO?
• Is it ok when media recommend articles to a person with racial opinions supporting his or her orientation?
• Should public agencies be contacted if a person drives drunk?
• Is it ok for a gaming app to capitalize on persons with gambling addictions?
Billion Global Spending on AI by 2021
Percent Increase Active AI Startups since 2000
Fold Increase Investment in AI since 2000
*Statistics according to IDC, Stanford University,and towardsdatascience.com
The AI Sustainability Center invited partners, friends, and guests to a General Insights seminar on AI and Gender bias with keynote Robin Hauser, and a panel including Tonima Afroze (Klarna), Anna Berggren (TRR), Sara Övreby (Google), and Lena Ag (The Swedish Gender Equality Agency), followed by a Q&A.
”Algoritmer är det nya guldet för bankvärlden”
“AI-teknologin måste gå att lita på”
Located at ALMA – a shared space for entrepreneurs, artists, developers and creators.