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Writer's pictureKerrie Smit

The Ethical Implications of AI in Change Management

AI is rapidly transforming various industries, and change management is no exception. As organisations increasingly rely on AI-powered tools to support their change initiatives, it's crucial to consider the ethical implications that arise.

A professional man is holding a tablet device in an office

Potential Ethical Concerns

Bias and Discrimination

AI algorithms are trained on data, and if that data contains biases, the AI system may perpetuate or amplify those biases. This can lead to unfair treatment of employees or stakeholders during change processes.


In the early advent of AI training we saw the Microsoft bot Tay that went on Twitter in 2016 to learn language. Tay became racist and offensive within 24 hours of its launch. The bot was designed to learn from interactions with users, but it quickly learned to mimic and amplify harmful language and stereotypes. This early incident highlighted the challenges of creating AI systems that can interact ethically and responsibly in the real world.


It also demonstrates that there are aspects to training AI that might not be foreseen at the time of planning. If the biases are unknown, serious problems can arise.


Job Displacement

The automation of tasks through AI may lead to job losses or changes in job roles. It's essential to consider the ethical implications of job displacement and how to mitigate its negative effects.


In the automotive industry, robots have replaced human workers on assembly lines for decades, reducing the need for labour-intensive tasks such as welding, painting, and assembly. Over the years increasing automation has resulted in job losses for many workers in automotive manufacturing.


While AI-driven automation can improve efficiency and productivity, it's essential to acknowledge the social and economic consequences of job displacement. It's crucial for governments, businesses, and educational institutions to develop strategies for retraining workers affected by automation and create new job opportunities in emerging fields.


Privacy and Data Security

AI systems often rely on large amounts of data. Protecting the privacy and security of this data is crucial to prevent misuse and ethical breaches.


Deepfakes are a classic example of misuse of AI that result in a privacy breach. AI-generated videos and images showing fabricated and altered content are manipulated to falsify the opinions or actions of real people, often public figures. These can be used to spread false information and manipulate public opinion. When fake images are misused like this, serious reputational damage can occur. The deepfake is unethical because it presents false information as real; it can damage a person's reputation, career, and personal life; and it can be used to influence others' opinions.


Lack of Transparency

As most of us are not data scientists, chances are that AI algorithms will be complex and difficult to understand. This may lead to a lack of transparency that can make it challenging to determine how decisions are made in AI models, raising concerns about the accountability and fairness of the advice or output provided.


For example, if an AI algorithm is trained on historical data about the demographics and qualifications of employees in a particular field, then it may recommend candidates that match the historical profile. The AI algorithm is quite likely to recommend white male candidates over all other equally qualified candidates because the bias in previous hiring practices has produced the biased data that trained the algorithm.


Without full transparency into this potential bias, human oversight might not pick up and correct the unfairness in time.


Ethical Decision-Making

AI systems may not be able to fully understand or consider the ethical implications of their decisions. This raises questions about who is ultimately responsible for the ethical outcomes of AI-driven change initiatives. Before using AI models in change management, we need to be certain of the parameters in use that could have ethical implications.


In a current, real-world example, an AI system was recently used by a large transport and logistics company to map out more efficient delivery routes. While the system replotted driver regions, schedules and territories, it had little insight into the detailed factors that drivers learn when working their routes regularly.


In two adjacent suburbs, previously allocated to two separate drivers, one suburb only had a single main road in and out, leading to traffic chaos and delays if approached from the wrong direction at the wrong time of day. The AI system plotted these suburbs to a single driver, not having the human experience to know that traffic conditions made that workload unmanageable.


The AI system was also unaware of customer usage that drivers get to know by experience. Drivers need to make enough deliveries to clear sufficient space in their trucks before handling pickups from customers. This means that they need to deliver at least an equal volume of goods prior to customers' pick up times. Customer pick up times are determined by another set of human factors such as the work schedules in their premises.


Routes that were designed without consideration for the volume of goods in and out were putting truck drivers under time pressure. As professional drivers who work 8 - 12 hour shifts on the roads, truck drivers are accountable for their safe and professional conduct, including driving at safe speeds. Unmanageable workloads created by the AI system potentially put the drivers' accountable conduct at risk.


With the AI design of delivery routes and schedules, nuances of the human experience were consistently missed. The implementation of these new schedules across an entire Australian city, was reversed after a single week.


Mitigating Ethical Risks

To address ethical concerns, organisations should ensure data quality and fairness by carefully curating, evaluating and testing the data used to train AI systems to minimise bias and discrimination.


Establishing clear ethical guidelines for the use of AI in change management, including principles for data privacy, transparency, and accountability needs to be backed up by investing in training on ethical AI practices to help employees understand and address potential ethical issues.


Organisations should actively promote transparency. It is only by being transparent about the use of AI in change management, explaining how AI systems are used and the decision-making processes involved, that human oversight can be fully informed. Maintaining human oversight of AI-driven change initiatives is essential to ensure ethical decision-making and minimise potentially harmful biases and practices.


The Ethical Implications of AI in Change Management

There appears to be great benefit from the use of AI in automating repetitive tasks, optimising processes, drawing insights from data and predicting future trends and outcomes. In change management it's conceivable that AI can help organisations respond more quickly to change in their market or operating environment, and improve the human experience of the change process through greater personalisation and access to 24 hour information and support channels.


However, in order to ensure AI does not create negative effects for the progress of a change initiative, the ethical considerations need to be kept to the forefront and managed through human oversight. By anticipating and proactively addressing ethical implications, organisations can potentially harness great benefits of AI in change management while minimising risks and ensuring that change initiatives are conducted in a fair, transparent, and ethical manner.


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