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While the dangers posed by Artificial Intelligence (AI) have been recognized from the time of its inception, it is only now that we see more attention being given to ethics in using AI. In a survey conducted by deeplearning.ai co-founder Andrew Ng in August 2020, it was revealed that explainable and ethical AI was one of the four important challenges that the AI community should be working on.
AI in Aviation
Although AI has been around for more than 60 years, advances in computing and accessing data have enabled many industries to unlock the potential of machine learning algorithms. The aviation industry is not lagging behind either. Improvements, customer service, and forecasting tools are some of the areas where machine learning is applied. Here are some use cases:
- In Air Traffic Management, machine learning can mine huge amounts of historical data to help the ground operators and pilots in the cockpit make better decisions.
- AI can aid in improving the accelerating climate crisis by optimizing trajectories.
- Increasing prediction accuracies can take inefficiencies out of operations, which in turn, can help offset carbon emissions.
- With a visual recognition system, aircraft inspections and fault detections can be automated and made more efficient.
- In personnel management, AI can give crew feedback on their fatigue levels and identify areas where machine input is possibly superior to human judgment.
- Unmanned aircraft or drones can be used for dropping medicines, especially during disaster relief or while navigating through unhospitable terrains.
While we list a few areas where AI would be useful in aviation, caution should be exercised. In this section we talk about some of the gray areas where we need to use AI responsibly.
Autonomous aircraft are likely to have fewer challenges than autonomous cars because the airspace is simpler – no pedestrians, signs or traffic lights, no detours due to construction work, etc. The risk of replacing people in the cockpit arises in extreme situations such as landing of the aircraft in a storm with no visibility. A machine would probably be as successful as a human in these scenarios, but would a machine’s decision be acceptable?
With fatigue management systems for crew members, the problem with using AI will be that pilots rely solely on machine inputs. Would this make the pilots less attentive? In addition, there is a problem of invasion of privacy as the health status and alert level of the crew are constantly monitored. Is it acceptable for the common good to compromise privacy?
Visual recognition systems or neural networks, which rely on more and more data in order to “learn” and offer greater precision, also have to be validated. When is it good enough to be implemented? Can you break with some unexpected dates? The COVID pandemic can be seen as an example that is referred to as an “unstructured break” in the time series data. We need fail-safe options in case such algorithms fail.
The biggest obstacle, however, is cybersecurity. As digital technologies increasingly replace human labor in dealing with routine manual tasks, security must be more reliable and fail-safe. The stakes play in a completely different league compared to the social media account.
The way to the future
In aviation today, AI is limited to non-critical operations. The challenges posed by COVID offer the opportunity to revise existing manual processes and introduce new technologies in order to create a scalable, economically and ecologically sustainable aviation environment. The community should form a committee that can develop an action plan. The establishment of a committee can effectively address some of the dangers that we can already foresee.
Some other elements of the action plan could be awareness campaigns about the benefits of AI and demystification campaigns. You can build infrastructure to set up new aerospace platforms to collect data that was previously only available through satellites. Access to data should only be granted to stakeholders approved by the Committee, interpret the recommendations of analytical tools and change management (e.g. health practices that should be adopted during a pandemic). Ethics training should be mandatory – topics should include identifying gray areas and desirable ethical outcomes in such situations.
The aviation industry is hardest hit by the ongoing pandemic. We need to use it as an opportunity to accelerate the adoption of AI. In addition to better decision-making aids, industrial and operational efficiency, we also need to ensure that we use AI responsibly.