What is Ethical AI?
Ethical Artificial Intelligence (AI) is the practice of deploying AI solutions that account for bias, ethics, and security across all aspects of the implementation plan. From data to design, all the way through deployment and decision making. But what does it really mean to have ethical AI?
Examples of Ethical AI Development Practices
1. AI Trained on Diverse Databases
Databases are essential training tools for Artificial intelligence (AI). But they really go hand in hand. AI can make databases smarter. For example, traditional database optimization rules such as cost estimation, join order selection, grip adjustment, index, and view selection cannot meet the high-performance requirements for large database instances, diverse applications, and diverse users, especially in the cloud. Fortunately, learning-based methods can solve this problem. In simple terms, AI can make very complex and hard to use databases easy to use without compromising on the depth and scope of the dataset.
On the other hand, database technology can optimize AI models. Artificial intelligence, for example, is difficult to deploy in real-world applications because developers must write complex code and train complex models. Database technologies can be used to simplify the use of AI models, accelerate AI algorithms, and enable AI features in databases.
With a well-developed database, AI algorithms can be trained in ways that human beings simply cannot match. For example, a face emotion AI detection algorithm can be trained to consistently analyze human emotion and mixed emotion with some level of objectivity.
Artificial intelligence (AI) is no longer a prediction of the future, it is part of business practices.
Industry experts note that the model does not pick up on bias, but rather “learns from the exposed data.” Therefore, a data set that targets a specific category, class, gender, or skin color can be an inaccurate model.
Dealing with source bias starts with recognizing potential problems and building a diverse team. This means not only gender diversity but also racial diversity, different skin colors, and different life experiences.
Businesses need tools and help to recognize the problem and assess potential bias. Diverse databases are the solution.
2. AI with Secure User Data
As artificial intelligence advances, it empowers us to use personal information in a way that could compromise our interests—bringing AI privacy to new heights of power and speed.
Data collection and processing models can influence AI and algorithm discrimination in several ways:
- Data governance requirements such as fairness or loyalty obligations, can discourage the use of personal information that is unfavorable or unfair to the individual to which the data belongs.
- Data Transparency or Disclosure Rules affect people’s rights to access related to them
The data collection and exchange rules can reduce data aggregation allowing conclusions and predictions, but this can sacrifice some of the benefits of large and diverse data sets.
Here’s how AI organizations, businesses, and lawmakers regulate how AI is developed and used:
This detailed disclosure of what and how data is collected, utilized, and safeguarded improves transparency for both parties. Consumers can better understand how their data is used, and companies learn how consumers respond to this usage.
Transparency provides advance notice of algorithmic decisions, while explainability includes retrospective information about the algorithm’s use in a particular decision. As our understanding of the comparative strengths of human and machine capabilities grows, there is a “man in the loop” in the decisions that affect people’s lives that provides a way to combine the power of machines with human judgment and empathy.
One clear lesson from the AI discussion is summarized in a review of best practices conducted by scientist Nicole Turner-Lee’s work with Paul Resnick and Jeanie Barton.
It is important for operators and algorithm designers to always ask themselves. Are some groups of people getting worse as a result of the development of algorithms or unintended consequences?
These Proactive Steps are Necessary for Ethical AI
Due to the difficulty of predicting machine learning outcomes and the algorithmic decisions of reverse engineering, no single action can be completely effective in preventing unwanted effects. So, if your algorithmic decisions are consistent, it’s a good idea to combine measures and work together.
Proactive measures such as transparency and risk assessment combined with retrospective audits and human decision analysis can help identify and address unfair outcomes. Combinations of these measures can complement each other and exceed the sum of their parts as a whole.
Risk assessment, transparency, accountability, and auditing will reinforce existing remedies for actionable discrimination by providing documentary evidence that can be used in legal proceedings. However, not all algorithmic decisions are sequential, so these requirements should depend on objective risk.
3. Continuously Improving AI
Artificial intelligence and machine learning techniques for validation and improvement have already been successfully applied in various fields. For example, botanists have used AI to create smart greenhouses that benefit from autonomous control, planning, and monitoring.
Meanwhile, machine learning researchers are exploring the possibility of using AI to detect anomalies on railroad tracks and bring trains to a standstill before crashing.
While measurement is the first step to improving any process, the next step is not always clear—especially in complex production environments where AI autonomy can help. What is important is to remember that good AI depends on human direction. AI developers must continuously ask themselves how to further improve their algorithms and applications.
MoodMe’s AI Ethics Promise
The ethics of artificial intelligence is especially important for brands that offer emotion detection, augmented reality capabilities, and similar applications.
MoodMe was founded with AI ethics and privacy in mind.
Our first face tracker from 2015 used a proprietary dataset with a balanced mix of gender, age, and ethnicity. We are continuously developing our deep learning algorithms to create a compressed neural network capable of detecting surrounding AI, emotion, gender, age, and ethnicity. Our dataset is now much larger than what we used way back in 2015. We now have more than 1 million faces from all over the world with a mixed variety of ages, genders, and ethnicities. We promise to continuously monitor and develop to ensure that the most stringent diversity criteria are met.
We are proud to say that our emotion detection algorithm detects emotions with equal accuracy on faces of all nationalities.
MoodMe understands and practices the spirit of AI ethics. There’s no telling of how big AI’s role in business and life will become. As AI continues to develop it can be found in more and more facets of our daily lives. MoodMe promises to continuously grow and invest in the future of ethical AI.