What Organizations Need to Responsibly Curate AI
Many organizations utilize artificial intelligence (AI), often without full awareness of its implications. The focus is on the responsible use of AI technologies and understanding how organizations can effectively implement them.
The Socio-Technical Challenge of Trust in AI
Earning trust in AI poses a significant socio-technical challenge, particularly concerning the human elements involved. A comprehensive approach to curating AI responsibly consists of three essential components: people, processes, and tools.
Understanding the People Component
At the core of a successful AI strategy is the right organizational culture. Establishing effective AI governance processes is crucial for managing tasks such as gathering inventory, assessing risks, and ensuring the models reflect the correct intent.
Three Key Tenets for Responsible AI
Among the three components, people represent the most challenging aspect of implementing responsible AI. Here are three key tenets to consider:
Tenet 1: Humility
Organizations must approach AI with tremendous humility. This involves recognizing the need to unlearn traditional perspectives regarding decision-making and inclusivity in AI discussions. A growth mindset is essential, fostering an environment of psychological safety that encourages open dialogue about the challenges AI presents.
Tenet 2: Varying World Views
Recognizing that individuals bring different world experiences to the table is vital. Organizations should value the diversity of their workforce, acknowledging that perspectives on gender, race, and lived experiences all influence AI development. It is important to ask critical questions such as: Is this appropriate? Is this the right data? What could potentially go wrong?
Tenet 3: Multidisciplinary Teams
Organizations should foster multidisciplinary teams in AI development. This involves including individuals from diverse fields such as sociology, anthropology, and law, which are essential to creating responsible AI solutions.
Recognizing Bias in AI
A common misconception is that AI development is solely about coding. In reality, over 70% of the effort involves determining the appropriateness of the data used. Data itself is a product of human experience and is inherently biased. Understanding this bias is crucial; as one expert puts it, AI serves as a mirror, reflecting back the biases of its creators.
The Importance of Transparency
Organizations need to maintain transparency regarding their AI models. This includes clarifying the decision-making process behind data selection, methodologies, and accountability. Essential details to disclose may include:
- Intended use of the AI model
- Source of the data
- Methodology employed
- Audit frequency and results
It is important for individuals involved in AI to be self-aware and recognize when their values may not align with the AI outcomes. As it is often stated, “All data is biased.” Transparency about data choices is key to responsible AI development.
Concluding Thoughts
Trust in AI is earned, not given. Organizations should engage in difficult conversations about biases and ensure that creating responsible AI models requires continuous effort and introspection.
