What ethical principles should artificial intelligence follow?

As data science becomes more sophisticated and consumers increasingly demand a more personalized customer experience, AI is a tool to help 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 address the ethical challenges that remain. As this technology evolves, one question that should be kept in mind by all leaders seeking to implement an AI strategy is how to maximize the use of AI within the enterprise in an ethical and responsible manner. To implement and scale AI capabilities that deliver a positive return on investment, while minimizing risk, reducing bias, and driving AI to value, organizations should follow four principles:

1. Understand goals, objectives and risks

About seven years ago, an organization released what they called the "hype cycle for emerging technologies," predicting the technologies that would transform society and business over the next decade. Artificial intelligence is one of these technologies. The release of the report has prompted companies to scramble to prove to analysts and investors that they are AI-savvy, and many are beginning to apply AI strategies to their business models. However, sometimes these strategies prove to be poorly executed and can only be used as an afterthought to existing analytical or numerical goals. This is because businesses do not have a clear understanding of the business problem they are looking for AI to solve. Only 10% of AI and ML models developed by companies are implemented. AI is lagging behind the historic disconnect between the business in question and the data scientists who can use AI to solve the problem. However, as data maturity has increased, companies have begun to integrate data translators into different value chains, such as marketing business needs to discover and transform results. That's why the overarching principle of developing an ethical AI strategy is to understand all goals, objectives, and risks, and then create a decentralized approach to AI within the enterprise.

2. Addressing prejudice and discrimination

Businesses big and small have suffered reputational damage and customers don’t trust them because AI solutions have never been properly developed to address bias. So businesses creating AI models must take pre-emptive measures to ensure their solutions don’t cause harm. The way to do this is to create a framework to prevent any negative impact on the algorithm's predictions. For example, if a company wanted to better understand customer sentiment through surveys, such as how an underrepresented community perceives their services, they might use data science to analyze these customer surveys and recognize that some A percentage of responses were in languages other than English, the only language the AI algorithm could possibly understand. To solve this problem, data scientists can not only modify the algorithm, but also incorporate the complex nuances of the language. If they can understand these linguistic nuances and combine AI with more fluent language to make these conclusions more actionable, businesses will be able to understand the needs of underrepresented communities to improve their customer experience.

3. Develop a full range of basic data

AI algorithms are capable of analyzing large datasets, and businesses should prioritize developing frameworks for data standards used and ingested by their AI models. To successfully implement AI, a holistic, transparent and traceable dataset is essential. AI must account for human interference. Such as slang, abbreviations, code words, and many more words that humans have developed on the basis of continuous evolution, each of which can make highly technical artificial intelligence algorithms go wrong. AI models that are unable to handle these human nuances end up lacking the overall dataset. It's like trying to drive without a rearview mirror, with some needed information, but a lack of key blind spots. Businesses must find a balance between historical data and human intervention in order for AI models to understand these complex distinctions. By combining structured and unstructured data and training AI to recognize both, a more comprehensive dataset can be generated and the accuracy of predictions improved. Further, third-party auditing of datasets can be an added benefit, free from bias and discrepancies.

4. Avoid the "black box" of algorithm development

Approaches For AI to be ethical, it needs to be completely transparent. To develop AI strategies that are simultaneously transparent, explainable, and explainable, companies must open the "black box" of code to understand how each node in the algorithm draws conclusions and interprets results. While this sounds simple, achieving this requires a robust technical framework that can interpret model and algorithm behavior by looking at the underlying code to show the different sub-predictions that are being generated. Businesses can rely on open source frameworks to evaluate AI and ML models across multiple dimensions, including:

Feature analysis: to assess the impact of applying new features to existing models

Node Analysis: Explain a subset of predictions

Local Analysis: Interpret individual predictions and matching features to improve results

●Global Analysis: Provides a top-down review of the overall model behavior and key features. Artificial intelligence is a complex technology with many potential pitfalls if businesses are not careful.

A successful AI model should prioritize ethics from day one, not an afterthought. Across industries and businesses, AI is not one-size-fits-all, but one common denominator that should make breakthroughs is a commitment to transparent and unbiased forecasting.