By Prangya Pandab - July 06, 2022 4 Mins Read
Experimenting with AI is a critical next step for businesses seeking digital disruption. It frees employees from tedious tasks and allows certain activities to scale in ways that were earlier financially unfeasible. It is, however, not to be taken lightly. AI applications must be built appropriately with careful monitoring to minimize bias, ethically questionable decisions, and poor business outcomes.
Although Artificial Intelligence (AI) has immense promise within enterprises, it is still mainly a problem-solving solution. AI is prone to misuse because many companies lack the funding, skills, and vision to apply AI in a truly disruptive way.
However, just because AI isn’t evident in day-to-day operations doesn’t imply it isn’t at work elsewhere within the company. Ethical flaws in AI, like many other ethical challenges in business, are often hidden. Whether on purpose or not, an AI project or application that crosses ethical lines can be an optical and logistical nightmare. The key to avoiding ethical issues in AI is to establish corporate governance from the start.
Developing AI with Trust and Transparency
There have already been several instances of AI gone wrong. These incidents not only make for poor headlines and social media backlash but also jeopardize other legitimate AI use cases that will never be realized if the technology is still viewed with suspicion. For example, AI has the ability to enhance cancer diagnosis and identify individuals at high risk of hospital readmission, requiring further support. Businesses must learn to develop AI people trust to gain the full benefits of these powerful technologies.
Ethical AI is Impossible to Achieve in a Vacuum
If AI applications are implemented poorly, they can have far-reaching consequences. For instance, this is a common occurrence when a single department begins to experiment with AI-driven activity without monitoring. Is the team aware of the potential ethical ramifications if the experiment goes wrong? Is the implementation in line with the organization’s current data access and retention policies?
It’s difficult to respond to these questions without supervision. Without governance, it can be considerably more challenging to bring together the stakeholders required to address an ethical breach if one occurs. Oversight should not be viewed as a constraint on innovation but rather as an essential check to ensure AI operates within a set of ethical bounds. In organizations with Chief Data Officers (CDO) or the CIO, if there isn’t one, oversight should ultimately be their responsibility.
Also Read: Strategies for Reducing Compliance Expenses with AI and Automation
Always Have a Plan in Place
The organizations at the center of the worst headlines about AI projects gone wrong often have one thing in common: they weren’t prepared to address questions or explain decisions when things went awry. This can be remedied by oversight. When the very top of a business has a good understanding of AI and a healthy mindset, there’s less chance of being
Mandatory Testing and Due Diligence
With a little more patience and testing, many of the classic examples of AI bias could have been avoided. Before the product is released to the public, more testing can uncover bias. Furthermore, any AI application should be thoroughly evaluated from the start. Due to its complexity and undetermined possibilities, AI must be employed carefully and strategically.
Establish an AI Oversight Function
Businesses spend a lot of time and money managing access to sensitive documents to preserve their customers’ privacy. Their records teams classify assets and set up infrastructure to ensure that only the right departments and job roles can access them. This structure could be used to establish an AI governance role within a company. A dedicated team can estimate the potential impact of an AI application and how often and by whom its outcomes should be reviewed.
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Prangya Pandab is an Associate Editor with OnDot Media. She is a seasoned journalist with almost seven years of experience in the business news sector. Before joining ODM, she was a journalist with CNBC-TV18 for four years. She also had a brief stint with an infrastructure finance company working for their communications and branding vertical.
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