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Five Ways Businesses Can Make AI More Ethical

As nations across the world slowly reopen their economies after extended lockdowns, businesses will need to hit the ground running to operate in a new abnormal. One of the ways companies can count on meeting the acceleration with safety is by adopting smart tech, especially tools and platforms enabled by artificial intelligence.

However, because these tools and platforms are built on algorithms, there is concern that the use of AI technology might unconsciously result in and perpetuate biases. When it comes to this area, a business’s commitment to ethical operation is a must in a more transparent world where consumers are keenly aware of a company’s track record and business conduct.

What can businesses do to effectively tackle this challenge? How can organizations safely deploy platforms enabled with AI to do more with less while ensuring that they are always doing the right thing?

Enterprises can undertake five best practices to ensure the adoption of AI does not go against the established rules of ethical corporate behavior.

First, organizations must have a clear understanding of what practicing ethical AI means to them and communicate this clearly to stakeholders. These communications should convey the core values that define a business, whether it is transparency, customer delight, or people-focus. An ethical application of AI will then mean that none of these values are compromised or watered down irrespective of the corporate function that executes it.

Second, businesses must invest in ensuring a more ethical application of AI by employing a chief AI ethics officer. This clearly defined position would be in charge of overseeing, limiting, and assessing how AI is embedded into an enterprise system. This would help to restrain a company from allowing AI to be used to carry out inappropriate or controversial functions, such as facial recognition.

Third, companies need to incorporate an AI ethics and quality assurance review as an integral part of the product development and release life cycles, including a focus on various use case scenarios and resulting outcomes. Every new AI-enabled product should be examined from an ethical lens to confirm that it adheres to established protocols around data safety and compliance.

Fourth, enterprises can ensure ethical AI deployment by turning to the customer. A cross-section of experts selected from a company’s advisory council can be leveraged in the testing of newly created AI enabled tools. Their inputs and experience can be funneled back into the product cycle to help the solution become more transparent, fair and impartial, safe, and ethical.

Finally, the fifth way for a business to adopt AI while staying within the confines of regulatory compliance and ethics is to remain transparent about how data is used to build algorithms. Since these algorithms tend to be opaque and complex, an enterprise, while balancing IP interests, may consider going beyond the call of duty to explain and describe to its customers what data is being sourced, and for what purpose.

Making a clearer link between the value data offers to build efficient algorithms and its potential ability to deliver superior customer experience will go a long way in assuaging customer concerns about the ethical deployment of AI to deliver products and services.

The original article was published here.

To support for AI technology and development for social impact, Michael Dukakis Institute for Leadership and Innovation (MDI) and Artificial Intelligence World Society (AIWS.net) has developed AIWS Ethics and Practice Index to measure the ethical values and help people achieve well-being and happiness, as well as solve important issues, such as SDGs. Regarding to AI Ethics, AI World Society (AIWS.net) initiated and promoted to design AIWS Ethics framework within four components including transparency, regulation, promotion and implementation for constructive use of AI. In this effort, Michael Dukakis Institute for Leadership and Innovation (MDI) invites participation and collaboration with think tanks, universities, non-profits, firms, and other entities that share its commitment to the constructive and development of full-scale AI for world society.