Artificial intelligence (AI) has created a tremendous impact on various businesses across the globe. From decision making and automation to understanding customer behaviour, AI has transformed the way we function. Artificial intelligence has given us various products like Apple’s Siri, Amazon’s Alexa, Google’s Home Assistant, Pandora’s AI, IBM Watson, Nest, autonomous cars, Google Maps, and many others. Considering the tremendous impact of AI in our life, AI ethics is something which everyone is focusing on.
Large organizations and businesses worldwide are looking to embrace a robust ethical AI strategy for future developments. Organizations are making use of data and artificial intelligence for building highly scalable and powerful solutions. However, this involves various regulatory, reputational, and legal risks as well.
It is important for businesses to understand the potential risk associated with the use of data and artificial intelligence and concentrate on the ethical use of AI. There is no doubt that the use of AI products has been beneficial to human beings. However, there are certain ethical challenges that need to be taken care of.
Examples
- Data biasness in AI algorithms can compromise data ethics and result in discrimination.
- AI deals with user sensitive information which needs to be protected.
- Malicious individuals may compromise user security and safety.
Deloitte research mentions that one out of three cyber security managers rate ethical threats as one of the top three challenges of AI enterprises.
Some case studies about the compromise of AI ethics by top organization around the world
- Regulatory bodies are investigating Goldman Sachs for making use of an AI algorithm that discriminates between men and women by granting a higher credit limit to men compared to women on their Apple cards.
- Los Angeles is suing IBM for illegally collecting and selling user data to marketing companies through its Weather Channel App.
- Facebook gave access to the personal data of more than 50 million Facebook users to Cambridge Analytica, a political consulting firm.
Considering the risks associated with the compromise of AI ethics, big tech companies such as Facebook, Microsoft, Google, Twitter, and several others are setting up different teams to ensure ethical use of AI by following the right ethical standards.
Building ethical AI is not an easy task and is a multi-step process. It needs an ethical way of developing AI systems that is free from prejudice and based on ethical standards. Also, organizations should train ethical AI models using a full array of data.
Here is the step-by-step process of developing ethical AI systems.
Step 1: Leveraging an existing framework
- The key to the success of creating a powerful AI ethics program is to make use of a present infrastructure.
- For example, a data governance board can help assemble issues related to data breaches, privacy, cyber threats, and compliance.
- Also, an AI ethics committee needs to be created by organizations, if it does not exist.
Step 2: Creating a tailor-made AI ethics framework
- The next step is creating a robust data and AI ethics framework that is tailor-made for your industry.
- This framework should include ethical standards for AI that will have a governance structure.
- It is also imperative to establish key performance indicators (KPIs) and a QA control program to measure the effectiveness of the strategies that are being implemented.
Step 3: Taking a leaf out of the success achieved in the healthcare industry
- The healthcare industry has been focused on eliminating the risks associated with AI ethics for a long time.
- For example, in the healthcare industry, a patient is only given treatment once he/she has understood everything about the procedure that is to be carried out and given a written consent for the same.
Step 4: Making life easy for product managers
A powerful framework for AI ethics will provide guidance at a high level. It is important to provide guidance pertaining to the products to product managers at a microscopic level. This will ensure that they are able to make better decisions depending on the situation in front of them. For this purpose, customized tools need to be created to help the product managers evaluate things better.
Step 5: Creating awareness in the organization
After creating the AI ethics model, it is important to let various departments in an organization know about it, especially the ones involved in handling data and AI products.
Step 6: Rewarding the employees for identifying risks to AI ethics
For AI ethics principles to be implemented successfully, it is important for an organization to reward those employees who identify potential risks to AI ethics at the right time. Rewarding and praising people for their efforts will influence others to do the same.
Step 7: Keep an eye on the impact of AI products
- It is important to monitor and track the use of data and AI products in the market.
- Even if the products are developed according to the ethical standards, it may happen that they are unethically used in the market.
- Also, during the entire process, it is important to keep the stakeholders informed and engaged.
It is crucial for all organizations that deal with artificial intelligence to understand that AI and ethics go hand-in-hand. In order to mitigate the risks involved in AI, the right codes of conduct need to be implemented and followed.
