Ethical Considerations, Risks and Limitations

Misrepresentation of cultural practices

• Risk of perpetuating biases, inaccuracies, and cultural distortion if AI models are not trained properly on Indigenous data and with Indigenous involvement. 
• Threats to Indigenous data sovereignty, intellectual property rights, and environmental impacts from AI development. 
• Threats to more culturally responsive teaching practices that ensure meaningful capacity building and inclusion rather than tokenistic gestures. 

Misappropriation of traditional or sacred imagery

• Misuse of generative AI to appropriate Indigenous cultures and knowledge systems without consent, highlighting the importance of Indigenous data sovereignty, ethical AI development, and increasing Indigenous voices in shaping these technologies.

Cultural appropriation and copyright infringement

• Theft and appropriation of Indigenous intellectual property, Indigenous art, and cultural identities by AI systems trained on data scraped from the internet can lead to further exploitation and marginalization of Indigenous peoples' cultures, knowledge systems, and lived realities.

Indigenous language reclamation and intergenerational learning

• Gen-AI language tutoring to help practice an Indigenous language.

Cultural preservation

• Digital archiving tools that can organize and classify photos, recordings, and documents under Indigenous governance .
• Restoration of damaged historical materials (recordings, photos etc.) with GenAI assistance .
• GenAI assistant interactive experiences grounded in Indigenous cultural teachings .

Indigenous-led environmental stewardship

• AI/GenAI systems developed to complement Indigenous knowledges in managing natural resources and monitoring weather patterns.  

Energy consumption during AI model training 
The process of developing and training AI models requires significant computational power, which in turn leads to extraordinarily high energy consumption. 

Carbon footprint and greenhouse gas emissions 
The energy-intensive nature of AI development and deployment results in a substantial carbon footprint, contributing to global greenhouse gas emissions. 

Water-intensive processes 
Generative AI requires vast amounts of water for manufacturing microchips and cooling data centres. 

Electronic waste 
The development and increasing use of AI technologies contribute to the growth of electronic waste and thus air, water, and soil pollution.  

Consequence of mining materials 
The hardware components necessary for AI technologies rely on huge amounts of copper and rare earth elements, the mining of which contaminates soil and groundwater with an array of toxic chemicals. 

Biodiversity monitoring and protection 
AI can help analyze large datasets to monitor species populations, habitats, and ecosystems more efficiently.  

Climate change mitigation 
AI can help optimize renewable energy systems, smart grids, industrial processes, and energy-efficient buildings, reducing waste, energy consumption, and greenhouse gas emissions. 

Precision agriculture 
AI can be used to optimize farming practices, reducing the need for water, pesticides, and fertilizers. 

Waste management and recycling 
AI can help optimize waste collection, sorting, and recycling processes, reducing the amount of waste heading to landfills or polluting the environment. 

Ecosystem restoration and conservation planning 
AI can analyze large environmental datasets and satellite imagery to identify areas in need of restoration, map ecosystem boundaries, and help plan and prioritize conservation efforts in a more data-driven and efficient manner. 

Datafication is the process of transforming all aspects of our lives, including our behaviours and attributes, into measurable digital data that can be collected, stored, analyzed, and used for various purposes. 

Datafication is driven by the widespread use of digital technologies, sensors, and connected devices, raising important questions about privacy, ethics, and security. To ensure responsible data collection, usage, and protection, we need appropriate legislation to establish safeguards and guidelines. However, GenAI users should be mindful that in many jurisdictions, online privacy legislation may be absent, incomplete, or out-of-date, so they may have limited recourse to combat bad actors and cyber criminals who use data for nefarious activities. 

Specific privacy concerns include: 

• data theft: data taken without consent; 
• data persistence: data outliving the humans who produced it; 
• data overreach: data used beyond the limits in which consent was originally given;
• data spillage: data collected on persons who were not the original target; and 
• data reidentification: data that has been un-anonymized. 

GenAI systems are often referred to as a ‘black box,’ meaning that there is uncertainty about how user data and inputs are used by the companies that own these systems. 

Assumed uses include: 

• additional system training where user data and inputs may be used to further train and refine the AI models; 
• consumer profiling where companies analyze user data to create profiles of their users so they can tailor their services and provide personalized experiences; and 
• customer service training where customer service teams study user data to improve support and service quality. 

However, these and other uses may not be clearly disclosed to users, including whether information will be shared with or sold to third parties. 

Given the lack of transparency around how data is used, users should be very cautious about the nature of information that they share with these systems and should not share any sensitive data (for example, personal, propriety or copyrighted information, health information). 

For the University of Regina’s relevant policy, please visit: Freedom of Information and Protection of Privacy | University of Regina  

From a security perspective, GenAI increases the potential for creating realistic scams. While malicious actors and cybercriminals have long used the internet and other technologies to deceive, manipulate, or dox (i.e., make public private or identifying information with the intention to inflict harm) individuals or organizations, GenAI makes it even easier for them to cause harm through more sophisticated and convincing tactics. 

For instance, GenAI can enable the production of highly convincing voice clones that mimic a target individual, which can then be used in various scams. These voice clones may be used in romance scams, where a scammer pretends to be a potential romantic partner and tricks the victim into sending money or personal information. Similarly, job scams can involve impersonating a hiring manager or recruiter to gain access to sensitive information or even request payment for fake job opportunities. 

GenAI's ability to produce large volumes of convincing content rapidly poses an increased threat as it enables cybercriminals to scale up their malicious activities. As a result, individuals, businesses, and governments must adapt by implementing enhanced security measures, such as multi-factor authentication and increased user awareness and education