- AI governance aims to make AI ethical, explainable, transparent and responsible.
- AI governance covers data quality, control and access, AI safety, legal and institutional structures, and ethical questions.
- Unchecked machine learning bias has been observed to profile people racially and financially and to misread sentiment.
- The black box problem is the difficulty of seeing why a model turned an input into a given output.
- A committee, not a single person, should be responsible for enacting and enforcing AI governance.
The goal of AI governance is to create ethical, explainable, transparent, and responsible AI. As a result, software gaps that exist between accountability and ethics are closed. AI governance has become an important practice stemming from AI’s explosive growth.
AI governance can define many areas:
- Data quality
- Data autonomy
- Data control
- Data access
- AI safety
- Determining inappropriate and appropriate AI automation
- Legal structures
- Institutional structures
- Moral questions
- Ethical questions
- Justice
Data and algorithms heavily influence the tools we rely on for everyday life. AI governance steers that influence and empower users to better understand and control how their AI software is used.
Does AI create bias?
AI has been shown to create bias, which is why AI governance is so important. AI is simply an output based on data inputs.
Gone unchecked, machine learning biases have been observed to racially and financially profile, return bad user information, misread sentiment, or even return information that is delivered inappropriately.
Why do we need AI governance?
AI and machine learning algorithms can now be found in a majority of sectors that influence our everyday lives. AI systems are used to improve our healthcare, financial systems, education, shipping, logistics, public safety, entertainment, and even farming and agriculture.
The wide exposure alone should be reason enough for all of us to demand visibility and control mechanisms, or “governance” for the systems that influence much of our lives.
Developing and enabling AI governance will help influence the best decision-making practices? with situations that rely on AI. That governance will help ensure that decisions are just and don’t interfere with basic human rights.
How do you Mitigate AI risks?
The best way to mitigate AI risks is to adopt AI governance practices and tools. Those tools need to provide visibility and controls to the systems that employ AI algorithms. Just as a manufacturing process contains quality-control systems to ensure that end products meet standards, an AI governance system ensures visibility and that controls are in place to enforce governance principles.
What is the Black Box Problem?
The black box problem comes from the way AI can naturally work in obscurity. AI models take an input, apply rules, and produce an output. But the obscurity (black box) that happens between input and output created can cause trust issues. What made the algorithm decide that? What information was ignored? How can we be sure that the output is accurate or valid?
AI governance creates the windows that allow us to see and understand the inner workings within the AI “black box” and helps create the external control mechanisms to alter how AI produces its output.
Who Should be Responsible for AI Governance?
A committee should ultimately be responsible to enact and enforce AI governance. No single person or role should be responsible; every team member plays an important role.
Government CEO or top management- Responsible for the AI Governance charter and assigning responsibility in the organization.
Board members, audit committee- Responsible for controlling and auditing the data.
General Counsel- Responsible for assessing risks behind legal aspects of the data and practices.
CFO- Responsible for assessing financial risks of the data and practices.
CDO - Responsible for the regular maintenance and assessment of new AI governance rules and enforcement practices.
Other leadership members - Responsible for ensuring general understanding and compliance of established AI governance practices.
What do AI ethicists do?
An AI Ethicist ensures trust and transparency by guarding against bias and unintended consequences. An AI ethicist’s primary responsibility is to improve the AI engineering practices by ensuring that the design, development, and implementation of AI systems have added ethical, social, and political safeguards.
Kpmg identified the AI Ethicist as, “Top 5 AI hires companies need to succeed in 2019.” The need for this role continues to be apparent as evidenced by the increasing variation of titles including:
- AI Ethicist
- Chief AI Ethics Officer
- Chief Trust Officer
- Ethical AI Lead
- Ethics Analyst AI Ethics and Governance
- Trust and Safety Policy Advisor.
This list of AI Ethics roles will and should increase as AI becomes more integrated in the systems we use.
AI Governance in H2O AI
H2O MLOps deploys, manages and governs models in production, with a model repository, version control, access control and logging for regulatory compliance.