Responsible AI: A Modern Framework For Ethical AI Practices
With great power comes great responsibility and Artificial Intelligence (AI) is one of the most powerful entities driving the world today. From automating conveyor belts and factory modules to powering self-driving cars and even helping us with our navigation systems, there are hardly any use cases left for AI. Transitioning from luxury to a necessity, AI finds its application even in some of the most life-dependent segments like healthcare.
With the heights and depths that it has reached in our lives, we have to gradually move from being in awe of the technology to question its functioning and decisions. Today, we need to ask ourselves as pioneers, market players, entrepreneurs, developers, AI specialists, and more one major question — how responsible are our AI systems?
Well, this is exactly what we are going to explore in today’s post.
Let’s get started.
What Is Responsible AI?
As the number of applications of AI increases and several aspects of lives are disrupted with the onset of AI, we need to raise a few questions on the impact it is creating. While AI systems offer immense benefits, there are also tons of concerns it gives rise to such as privacy breaches, workforce displacement, biased results, unmoderated decision-making and more. These are the issues that need to be addressed, discussed and fixed with planning, governance and oversight.
This is what responsible AI is all about. It is a framework that kindles the spark for all these practices to come together for the development and launch of AI modules that are ethical, accountable and 100% transparent. From delivering unbiased results to justifying its decisions, the responsible AI programming framework ensures AI is always kept on a consistent check. This is done with the implementation of primers such as governance, training, designing and monitoring.
With that said, achieving accountable AI cannot happen randomly. There has to be a systematic approach to developing responsible AI modules. Scrutiny amidst innovation should be a priority for stakeholders including government and federal agencies. That’s why we need to break down the principles of responsible AI to understand the concept better.
Principles Of Responsible AI
Comprehensiveness AI
No matter how advanced systems and machines are, they are still digital devices with tons of unforeseen vulnerabilities. Comprehensiveness AI is all about defining testing and governance metrics to ensure machine learning and AI modules are not exploited for breaches, intrusions and modifications.
Ethical AI
Bias is a plaguing concern in the development of artificial intelligence systems. This happens when AI training data used to train AI systems have a preconceived notion or prejudice about something (race, ethnicity, nationality, gender, marital status and more). As machines train with such biases, results are skewed accordingly as well. This makes the entire process of AI futile as the whole point of deploying machines is to bring in a sense of objectivity. Ethical AI ensures bias (voluntary and involuntary) is eliminated.
Explainable AI
When an AI module takes a specific decision, there has to be a justification to it. For instance, when an automated recruitment model decides to shortlist a set of candidates for an interview, what is the basis for that particular decision? What is the reason for other candidates getting rejected?
Well, it is on Explainable AI to answer all such questions. This principle explains the rationality behind any decision made by an AI system.
Systems in autonomous cars should be operating perpetually. Data from sensors, computer vision, RADAR and more should be collected and processed every single second to ensure cars take the most sensible driving decisions at any given point of time.
Even a lag of a few microseconds could result in the damage of life and property. In healthcare as well, AI systems should be perennially functioning. That’s where Efficient AI comes in. This ensures AI systems run continuously and rapidly respond to requirements and changes arising in an ecosystem.
The Importance Of Responsible AI
Responsible AI is inevitable today and for a better tomorrow as we dream of smart cities, smart power grids, and more. The metrics and governance protocols that we establish today as part of responsible AI framework will ensure that AI systems-
- Do not crash, malfunction or lag at the most crucial times
- Do not deliver prejudiced results
- Always maintain the integrity and confidentiality of data and the privacy of its users
- Are airtight in their functioning and that they are can’t be easily modified, manipulated or broken into and more
Responsible AI Practices
Taking cues from responsible AI primers, let’s understand some of the effective ways to implement responsible AI best practices.
Focus On Implementing A User-centric Design
While functionalities and aesthetics are inevitable, what should be a priority going forward are humane aspects like data and user privacy, confidentiality, security and trust. AI operations are still opaque and that creates a bridge among users, AI systems and companies that deploy the technology. That’s why the first step in achieving responsible AI is taking a humane approach to designing AI systems. When user needs and concerns are addressed and prioritized before AI processes, patronage and adoption rates will follow suit.
Make Results And Work Measurable
Despite being a universal concept, responsible AI is sometimes subjective as well. That’s why it is important to have measurable processes and systems to bring in responsibility in AI operations. These could include auditable tech frameworks, visibility and explainability.
Identify Metrics For Training And Monitoring Analysis
Ideally, it is recommended to have multiple metrics to measure differences in design, operations and ultimate experiences. This will allow you to have a holistic view of errors and disparities in your vision and the public perception of it. Having multiple contextual metrics that are in line with your goals and visions will ensure you have the most workable insights for your AI implementation.
Examine Raw Data
Garbage In Garbage Out is the standard operating protocol of AI systems. You feed them with bad data, you will get skewed and poor results. On the other hand, train them with quality data that is relevant, clean, unbiased and recent, your AI systems will offer the most effective results. Examining raw data is more crucial if you have unsupervised learning systems in place. So, pay attention to where your data stems from, how clean it is and how relevant it is to your goals and anticipated results.
Related Blog: Reforming Insurance Ecosystem With AI Technology
Understand The Shortcomings Of Dataset And Training Models
When a system learns to identify cats, it would identify correctly when it is repeatedly shown images of cats. However, when you show them an image of a dog in between, chances are that your AI system would go haywire. This is because it has limitations in making correlations. Because the performance of your model depends on the quality of AI training data, understand how the data you procured functions in tandem with your developed model. Understand such limitations and educate your users accordingly about their interpretation of your AI model.
Conduct Rapid Testing
AI systems can only be made more efficient with continuous optimization and this can happen with repeated testing. Conduct dynamic unit tests, integration tests, check for input drifts, work on iterative user testing, implement quality engineering principles and more to ensure your AI model is working as you intended it to.
Monitor Systems Even After Deployment
Apart from optimizing your model during development and training stages, you should also monitor your system after it is taken live and optimize for advanced efficiency. This is more effective in the fact that your system now has real-world data that is more contextual and relevant to work on. Based on results and experiences, update your model, come up with fixes or patches, expand the scope of your model to meet unforeseen real-world requirements and more.
Use Responsible AI Tools
There are effective tools to enforce the responsible AI framework. For instance, TensorFlow offers Explainable AI in its toolkit. Additionally, you could also perform bias testing and monitoring, implement predictive maintenance and more for your AI system. Also, find newer use cases for responsible AI with the current tools you have and implement internal protocols to roll out responsible systems.
The Future Of Responsible AI
Currently, the primary definition of responsible AI resonates more with fixing loopholes. However, the future has something different. It is when the transition will happen from fixing to anticipating. Proactive measures will be taken and AI systems will be used to study AI systems to anticipate flaws and build more secure and ethical models. Legalities and compliances associated with implementing AI will also become more stringent and companies would also define one SOP to implement responsible AI principles. The definition of responsible AI will be universal and more integral and there would be both product and user-driven metrics for more authenticity.
Wrapping Up
We believe this write-up was an eye-opener of sorts that makes you revisit your AI systems for explainability, ethics and accountability. This is enough to get the conversation started about responsible AI systems. Let’s be cautious in the world we create for tomorrow and let that start with modifications of today.
