AI Now 2017 Report - "Regulation, accountability, understanding and more regulation."2018 Feb 03
You can download here the copy of their conclusions, but the main tone it’s “Regulation, accountability, understanding and more regulation”. Check it out:
1) Core public agencies, such as those responsible for criminal justice, healthcare, welfare, and education (e.g “high stakes” domains) should no longer use “black box” AI and algorithmic systems. This includes the unreviewed or unvalidated use of pre-trained models, AI systems licensed from third party vendors, and algorithmic processes created in-house. The use of such systems by public agencies raises serious due process concerns, and at a minimum they should be available for public auditing, testing, and review, and subject to accountability standards.
2) Before releasing an AI system, companies should run rigorous pre-release trials to ensure that they will not amplify biases and errors due to any issues with the training data, algorithms, or other elements of system design. As this is a rapidly changing field, the methods and assumptions by which such testing is conducted, along with the results, should be openly documented and publicly available, with clear versioning to accommodate updates and new findings.
3) After releasing an AI system, companies should continue to monitor its use across different contexts and communities. The methods and outcomes of monitoring should be defined through open, academically rigorous processes, and should be accountable to the public. Particularly in high stakes decision-making contexts, the views and experiences of traditionally marginalized communities should be prioritized.
4) More research and policymaking is needed on the use of AI systems in workplace management and monitoring, including hiring and HR. This research will complement the existing focus on worker replacement via automation. Specific attention should be given to the potential impact on labor rights and practices, and should focus especially on the potential for behavioral manipulation and the unintended reinforcement of bias in hiring and promotion.
5) Develop standards to track the provenance, development, and use of training datasets throughout their life cycle. This is necessary to better understand and monitor issues of bias and representational skews. In addition to developing better records for how a training dataset was created and maintained, social scientists and measurement researchers within the AI bias research field should continue to examine existing training datasets, and work to understand potential blind spots and biases that may already be at work.
6) Expand AI bias research and mitigation strategies beyond a narrowly technical approach. Bias issues are long term and structural, and contending with them necessitates deep interdisciplinary research. Technical approaches that look for a one-time “fix” for fairness risk oversimplifying the complexity of social systems. Within each domain – such as education, healthcare or criminal justice – legacies of bias and movements toward equality have their own histories and practices. Legacies of bias cannot be “solved” without drawing on domain expertise. Addressing fairness meaningfully will require interdisciplinary collaboration and methods of listening across different disciplines.
7) Strong standards for auditing and understanding the use of AI systems “in the wild” are urgently needed. Creating such standards will require the perspectives of diverse disciplines and coalitions. The process by which such standards are developed should be publicly accountable, academically rigorous and subject to periodic review and revision.
8) Companies, universities, conferences and other stakeholders in the AI field should release data on the participation of women, minorities and other marginalized groups within AI research and development. Many now recognize that the current lack of diversity in AI is a serious issue, yet there is insufficiently granular data on the scope of the problem, which is needed to measure progress. Beyond this, we need a deeper assessment of workplace cultures in the technology industry, which requires going beyond simply hiring more women and minorities, toward building more genuinely inclusive workplaces.
9) The AI industry should hire experts from disciplines beyond computer science and engineering and ensure they have decision making power. As AI moves into diverse social and institutional domains, influencing increasingly high stakes decisions, efforts must be made to integrate social scientists, legal scholars, and others with domain expertise that can guide the creation and integration of AI into long-standing systems with established practices and norms.
10) Ethical codes meant to steer the AI field should be accompanied by strong oversight and accountability mechanisms. More work is needed on how to substantively connect high level ethical principles and guidelines for best practices to everyday development processes, promotion and product release cycles.