Ethics, Accountability, and Risk Analysis
Several months ago, a research paper that I had written was published in Strategic Finance. I read it over last night and decided to extract some of the material for today’s blog.
AI is transforming the role of accounting and finance professionals in unprecedented ways, and knowledge of how AI systems work is now a must for business school students and early-career professionals looking to lead the way in an increasingly technology-driven decision-making environment.
AI is broadly thought of as the creation of intelligent machines that can analyze large amounts of data and follow a set of rules defined by algorithms that enable machines to engage in problem-solving activities. AI systems include developing software to distinguish between and engage certain traits such as knowledge, reasoning, problem solving, perception, learning, and planning.
Given the focus on judgment and decision making, AI systems should be built around ethical principles that provide guidance on collecting data, processing it, and reporting the results. The collection of data for processing by AI systems should be done objectively and with confidentiality assured. From an auditing perspective, the data should be trustworthy and verifiable.
It’s important to stay ahead of the ethical issues surrounding the use of AI systems and the key related areas of AI that can be taught, such as the benefits of using AI including governance and accountability, ethics and risk analysis, regulation, and practical steps for integrating data analysis and AI into the accounting and finance curriculum.
Finance leaders should embrace new technologies to enhance digital transformations. CFOs play an important role in applying the new technologies to decision-making areas, such as predicting real values of financial assets and return on investment, identifying material misstatements that can be predicted through algorithms, and enhancing organizational efficiency.
Governance and Accountability
As I pointed out in “Ethical AI is Built on Transparency, Accountability and Trust” (Corporate Compliance Insights), governance and accountability issues fall into certain categories:
- Ethics standards for AI,
- Governance of the AI system and data,
- Internal controls over data,
- Accountability for unethical practices,
- Compliance with regulations, and
- Reporting findings directly to the audit committee or the board of directors.
In “AI: New Risks and Rewards” published in Strategic Finance, Mark A. Nickerson raises an important question: “Is the individual still ultimately going to be held to the highest standards of fiduciary responsibility and due care even in those instances where decisions and analyses were executed by AI systems?” Someone or some group needs to be held accountable to ensure proper standards are followed and adjustments are made based on an audit of AI systems.
Ethical Risks and Risk Management
AI systems are often referred to as a “black box,” as it can be difficult to fully understand the complex calculations and factors that lead to a particular decision or prediction. AI automates judgments—yes/no; right/wrong. These judgments should be made in an ethical way to promote responsible decision making.
Ethical risks in these AI systems range from how the data is collected and processed to the validity of conclusions reached from the analysis of data. Bias is a potential problem if, for example, ZIP codes are used to analyze good risks for mortgage loan lending. It could be that people living in one community are the least risky borrowers, but building their ZIP codes into an AI decision-making model may result in bias against those living in minority communities. Managing the ethical risks in an AI system, as outlined in “Managing Ethical Risks in AI Systems,” is similar to managing those risks involved in any decision-making system.
Given their broad scope, it’s essential that ethical risks be managed to enhance the reliability of decision making and to promote useful machine learning. Machines learn based on the data processed. If the data sample isn’t representative or accurate, then the lessons they learn from the data won’t be accurate and may even lead to unethical outcomes.
Risk management in AI systems starts by creating an organizational culture that supports good governance. According to the Institute of Internal Auditors (IIA), the governance of AI systems encompasses the “structures, processes, and procedures implemented to direct, manage, and monitor the AI activities of the organization” (Global Perspectives and Insights: The IIA’s Artificial Intelligence Auditing Framework). Governance structures vary depending on the scope of usage in an organization, but certain ethical principles should be followed to determine whether governance structures and processes are fulfilling their oversight role including accountability, responsibility, compliance, and meeting the standards of an ethical framework.
Role of CFOs
Grant Thornton’s 2019 CFO Survey (see “Technologies in the Finance Function”) found that a significant percentage of senior financial executives currently implement technologies such as advanced analytics (38%) and machine learning (29%). Year-over-year comparisons indicate that 42% of CFOs surveyed reported that their finance functions regularly make use of advanced and automation technologies in corporate development and strategic planning, compared to 18% in the 2018 survey.
There’s no doubt that CFOs will play an increasingly important role as financial leaders to ensure their organizations are using AI to facilitate strategic initiatives and operate more efficiently. Nearly 91% of respondents agree or strongly agree it’s the CFO’s job to ensure that their companies fully realize the benefits of technology investments. Ninety-five percent of respondents said their company’s CFO is a key stakeholder of enterprise transformation planning.
Financial leaders should embrace new technologies to enhance digital transformations. CFOs should play an important role in applying the new technologies to decision-making areas such as predicting the real values of financial assets and return on investment, identifying material misstatements in financial statements that can be predicted through algorithms, and enhancing organizational efficiency.
The U.S. Office of Management and Budget released a draft memorandum on January 13, 2020, providing guidance to agencies on how to approach regulation of industry’s AI applications. The key issue is that regulatory action shouldn’t hinder the expansion of AI. The memo states: “Agencies must avoid a precautionary approach that holds AI systems to such an impossibly high standard that society cannot enjoy their benefits. Where AI entails risk, agencies should consider the potential benefits and costs of employing AI, when compared to the systems AI has been designed to complement or replace.”
This cost-benefit analysis approach to ethical decision making in the AI realm suffers from the shortfalls of all utilitarian assessments. With AI, how can the potential costs of bias in the algorithms be measured? The potential harm to some stakeholders is real, including those who have been discriminated against because the algorithms include variables that may be harmful to their interests.
Effective regulation can be addressed by industry (for example, healthcare and financial services) or for all areas collectively. This is the challenge for regulators. Given the myriad of applications of AI systems in consumer and business decision making, the question is whether AI should be regulated and, if so, how to do it.
The usage of certain technologies should be regulated, or at a minimum monitored, to prevent the misuse or abuse of the technology toward harmful ends. Lawmakers and regulators in the United States have primarily pursued AI in autonomous or self-driving vehicles. The potential harm for injury is high if autonomous vehicles fail to protect lives in all possible scenarios.
Concerns about potential misuse or unintended consequences of AI have prompted efforts to examine and develop standards, such as the U.S. National Institute of Standards and Technology. This initiative involves workshops and discussions with the public and private sectors around the development of federal standards to create building blocks for reliable, robust, and trustworthy AI systems.
Ethics and AI should go hand in hand. If the data gathered by AI systems is biased or incomplete, the analyses using the data can’t be relied upon. Concepts such as fairness, objectivity, professional skepticism, and representational faithfulness underlie the ethics of AI and decision making in accounting and finance. Business school educators who want to truly prepare their students for tomorrow’s workplace must incorporate AI into as many areas as possible and be sensitive to the ethical issues discussed throughout.