As data science becomes more sophisticated and consumers increasingly demand more personalized customer experiences, artificial intelligence is a tool that helps businesses better understand their customers and audiences. But even if AI has all the potential in the world, that full potential may never be realized if we can't figure out how to solve the ethical challenges that remain.
As this technology evolves, one question that all leaders looking to implement an AI strategy should keep in mind is how to do so ethically and responsibly. Maximize the use of artificial intelligence within your enterprise.
To implement and scale AI capabilities that deliver positive return on investment while minimizing risk, reducing bias, and driving AI value, enterprises should follow the following four principles:
About seven years ago, an organization released what they called “ The Hype Cycle for Emerging Technologies,” predicts the technologies that will transform society and business over the next decade. Artificial intelligence is one of these technologies.
The release of this report has prompted companies to rush to prove to analysts and investors that they are proficient in artificial intelligence, and many companies have begun to apply artificial intelligence strategies to their business models. However, sometimes these strategies prove to be poorly executed and serve as little more than an afterthought to existing analytics or numerical goals. This is because businesses don’t have a clear understanding of the business problem they are looking for AI to solve.
Only 10% of AI and ML models developed by enterprises are implemented. AI lags behind the historic disconnect between the businesses with the problem and the data scientists who can use AI to solve the problem. However, as data maturity increases, enterprises have begun to integrate data translators into different value chains, such as marketing business needs to discover and transform results.
That’s why the first principle in developing an ethical AI strategy is to understand all goals, objectives, and risks, and then create a decentralized approach to AI across the enterprise.
Because artificial intelligence solutions have never been properly developed to solve the problem of bias, it has led to the failure of enterprises large and small. Reputations were damaged and customers distrusted them. So companies creating AI models must take preemptive measures to ensure their solutions don’t cause harm. The way to do this is to create a framework that prevents any negative impact on the algorithm’s predictions.
For example, if a company wants to better understand customer sentiment through surveys, such as how underrepresented communities view their services, they might use data science to analyze those customers Surveys, recognizing that a certain percentage of responses to published surveys are in languages other than English, which is the only language that AI algorithms may understand.
To solve this problem, data scientists can not only modify the algorithm but also incorporate the complex nuances of the language. If these nuances in language can be understood, and AI combined with more fluent language makes these conclusions more actionable, businesses will be able to understand the needs of underrepresented communities to improve their customer experience.
Artificial intelligence algorithms can analyze large amounts of data sets, and enterprises should prioritize their artificial intelligence models A framework for the development of data standards for consumption and ingestion. For AI to be successful, a holistic, transparent and traceable data set is essential.
Artificial intelligence must take human interference into account. Things like slang, abbreviations, code words, and more words that humans have developed based on continuous evolution, each of which can cause highly technical artificial intelligence algorithms to go wrong. AI models that are unable to handle these human nuances end up lacking an overall data set. It's like trying to drive without a rearview mirror, having some needed information but missing critical blind spots.
Enterprises must find a balance between historical data and human intervention in order for AI models to understand these complex distinctions. By combining structured data with unstructured data and training AI to recognize both, you can generate more comprehensive data sets and improve the accuracy of your predictions. Further, third-party auditing of data sets can be an added benefit, free of bias and discrepancies.
For artificial intelligence to be ethical, complete transparency is needed. To develop an AI strategy that is simultaneously transparent, interpretable, and explainable, companies must open the “black box” of their code to understand how each node in the algorithm reaches its conclusions and interprets its results.
While this sounds simple, achieving this requires a strong technical framework that can explain model and algorithm behavior by looking at the underlying code to show what is being generated Different sub-predictions.
Enterprises can rely on open source frameworks to evaluate AI and ML models across multiple dimensions, including:
Artificial intelligence is a complex technology with many potential pitfalls if businesses are not careful. A successful AI model should prioritize ethical issues from day one, not as an afterthought. Across industries and businesses, AI is not one-size-fits-all, but one common thread that should lead to breakthroughs is a commitment to transparent and unbiased predictions.
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