AI Transparency is the Future
For many reasons, transparency is potentially one of the most transformative dynamics of Artificial Intelligence. Explaining your AI technology and capabilities builds trust and accountability between you and your users in the models created. As we delve into transparency, AI programmers and managers come to understand the processes that are carried out by ML algorithms enabling them to create safe, reliable, trustworthy systems.
Introduction to AI Transparency
Transparency requires that the stakeholders understand how the algorithmic processes in ML work. The reasons are two-fold:
· First, that understanding will produce good results ahead of time.
· Secondly, stakeholders have the responsibility to validate that it does.
Transparency is often quite elusive when the ML and deep-learning systems are complex. Further, in certain industries, algorithms’ processes might be concealed as trade secrets. To determine the level of transparency that can be attributed to any system, data scientists assess its transparency based on the concepts of interpretability and explainability. Let’s have a look.
AI Transparency Terminology
Defined as a measure of how well humans can understand the processes of an algorithm, referring to the need to identify a particular problem and dataset before identifying an algorithm capable of handling data within that space. Based on that understanding, predictions are made on the result that the model will produce.
o Importantly, a relationship exists between an algorithm’s interpretability and the ability of human evaluators to understand its processes.
Without explainability, it is a challenge to hold developers and producers of algorithms to any standard that could make them accountable for the software they produce. Accountability is crucial when deploying algorithmic systems that make decisions that affect the lives of humans.
No explainability = No accountability
o Software engineers and data scientists must assume these roles and encode protective processes into the system pre-deployment.
o The need for posing the question of transparency during the development of AI systems becomes even more apparent.
Reasons for AI Transparency
The rationale for transparency is clear-cut and should include:
Ideally, interpretable models should be equipped to explain the answers they produce. People will want to inspect its parts to understand the mechanisms behind its decisions so they can replicate or improve its performance.
Industry-specific AI systems must adhere to all compliance specifications given by various governing bodies to maintain domain safety standards. A couple of examples:
- An AI system trained to work in a hospital setting should function optimally in real-life conditions without adverse effects, such as exacerbating the spread of diseases or interfering with the facility’s functions in a way that could make the environment unsafe for workers or patients.
- In situations where security is prioritized, AI must use adversarial learning to uphold optimal decisions developed on decision boundaries.
While adversarial systems are usually deployed against outside threats, developers may engineer them to detect potential biases in the data it uses.
Developers also have felt an ethical responsibility to ensure that algorithms are fair, do not discriminate, and adhere to the guidelines instituted by governments and other regulatory authorities. This is fueled by the desire to grant all people fair and equal access to the benefits of artificial intelligence without placing any individual or group at a disadvantage.
Another motive for the development of fair algorithmic models is the need to minimize the incidence of mismatched objectives. The research defines objective mismatch arising as “when one objective is optimized in the hope that a second, often uncorrelated, metric will also be optimized.” This unfavorable objective is often pursued because algorithm engineers lack sufficient insight into the functions of the models they deploy. The remedy for this involves the eradication of “black boxes” models that proceed by methods not easily learned or understood by humans auditing them.
Inside an ML system, sometimes there are two or more related features that conflict.
- The conflict between privacy and prediction quality means that an increase in a model’s ability to make accurate predictions might require it to lower its adherence to measures that protect privacy. As such, an optimal balance between multiple objectives must be negotiated by the developers through case-by-case decision-making processes.
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