The ethical issue of AI regulation in businesses aligns closely with UN SDG 9: Industry, Innovation and Infrastructure, particularly:

• Target 9.5: Enhance research and upgrade technological capabilities of industrial sectors in all countries, including encouraging innovation and substantially increasing the number of research and development workers. This underscores the need for investment in ethical AI research and governance frameworks.

• Target 9.c: Significantly increase access to information and communications technology (ICT), stressing equitable access to trustworthy and transparent AI technologies.

Additionally, SDG 16: Peace, Justice and Strong Institutions is relevant, especially Target 16.6, which calls for developing effective, accountable, and transparent institutions—parallels can be drawn to transparent governance of AI systems.

In Australia, companies are encouraged to follow voluntary AI Ethics Principles developed by the government. These principles highlight values such as fairness, accountability, and transparency, but since they are voluntary, compliance varies across industries. Some companies are proactive, developing internal policies and participating in international forums on AI ethics, while others lag behind, creating gaps in responsible use. The absence of consistent regulation leaves many businesses vulnerable to criticism and stakeholder pressure.

3. How the Ethical Issue Has Changed Over Time

Over the past decade, the conversation around AI has shifted from excitement about its ability to improve efficiency and productivity to concerns about its ethical use. Initially, AI adoption in business was viewed almost entirely as a technical innovation with little thought given to broader consequences. Today, there is much greater awareness of issues such as bias in algorithms, data privacy risks, and the potential misuse of AI in areas like recruitment, surveillance, or financial decision-making.

In Australia, government involvement has also evolved. In the past, there was almost no regulatory framework for AI. More recently, voluntary guidelines and safety standards have been introduced, and discussions are underway about whether mandatory regulations will eventually be needed. Businesses now recognise that the ethical dimension of AI is not just an optional extra but a central part of their responsibilities.

4. What Organisations Could Do (Resolutions and Solutions)

1. Introduce formal governance frameworks
Businesses could implement structured governance models for AI, ensuring that systems are monitored at multiple levels—strategic, organisational, and technical. This creates accountability and ensures decisions are aligned with both business goals and ethical standards.

2. Adopt ethics-based auditing
Companies should move beyond broad principles and conduct regular audits to evaluate AI systems for fairness, transparency, and bias. These audits help close the gap between policy and practice, providing measurable outcomes and building trust with stakeholders.

3. Strengthen board-level oversight
Organisations need leaders who understand AI and its ethical risks. Boards should include directors with expertise in AI ethics or create specialised committees to oversee responsible implementation. Publicly sharing AI policies can also increase transparency and credibility.

Australia has begun building its approach to responsible AI, with voluntary ethics principles and a national AI safety standard. Some companies, such as major telecommunications firms, are engaging at an international level to shape ethical frameworks, while others are slower to adapt.

The solutions outlined above would strengthen Australia’s position. Formal governance frameworks would provide structure, audits would ensure accountability, and board-level oversight would embed responsibility at the highest level of decision-making. These steps would not only reduce ethical risks but also enhance business reputation and competitiveness in global markets.

By aligning with SDG 9, businesses in Australia can ensure that innovation is not only technologically advanced but also socially responsible. By taking proactive steps now, organisations will be better positioned to meet future regulatory requirements and contribute to sustainable development goals for 2030 and beyond.

UNSDG's are important as they offer a frame work for innovation, sustainable growth and market opportunities especially for emerging economies. the UNSDG that would best relate to the topic of AI regulation within businesses is SDG 9 - industry, innovation and infrastructure, as AI can be used to benefit and enhance each of these traits within businesses. the main ethical issue associated with this SDG is that larger scale projects regarding infrastructure and industrialisation can lead to inequalities amongst different region if managed poorly, as well as this digital gaps between less looked after regions and worsen, created advantages for one region in terms of information and opportunity that are not present in other regions.

This paddle will delve into the ethical issues, real world issues and implications of the growing influence of AI in the world and how it should be regulated within businesses so that laws and ethics are upheld by various companies who seek to implement AI throughout their organisations

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2) Pair “privacy-by-design” data governance with media-literacy micro-workshops. Concretely: minimise/secure data, obtain meaningful consent, and offer opt-out; then run short peer sessions on AI misinformation and deepfakes with verification tips and reporting pathways. Evidence shows AI amplifies mis/disinformation risks but can also help detect it when users are trained.

What’s happening in Australia (now). Australia has voluntary AI Ethics Principles; the federal AI-in-government policy (in force from 1 Sept 2024) mandates accountable officials and public transparency statements by 28 Feb 2025. Privacy reforms (first tranche) passed in late 2024, and the government’s 2024 “Safe & Responsible AI” interim response signals that voluntary guardrails are insufficient and considers risk-based, mandatory guardrails in high-risk settings.

What difference our solutions make. We go beyond current, often voluntary settings by either making AIA/model cards routine and public, coupling privacy controls with user education, and enabling community auditing practical steps that raise transparency, reduce bias, and harden privacy while complementing emerging government mandates.

Artificial intelligence is one of the biggest technological advancements done by humanity, in the past few years ai has known a huge increase in popularity and can be found in various work fields ranging from healthcare to transportation. A plethora of businesses have started to adopt ai, the data reveals a positive trend in AI adoption among Australian small and medium businesses, 40% of SMEs are currently adopting AI, a 5% increase compared to the previous quarter (July – Sept 2024) and the proportion of businesses that are not aware of how to use AI has dropped by 2% to 21% (Australian Government, 2025). “The rapid rise of AI use is creating some very serious legal and ethical issues such as bias, discrimination, inequity, privacy violations, and—as creators everywhere fear—theft of protected intellectual property” (Ladwig, 2025, p. 53).

A survey done by ITBrief has shown that 62% of Australian organisations anticipate becoming more reliant on AI/ML decision making in the coming years, 66% believe there is currently data bias in their organisation and 84% believe they need to be doing more to understand and address data bias in their organisation, higher than any other countries surveyed (ITBrief Australia, 2023). “Imagine if your company used AI to quickly review applicants’ resumes to help identify the most qualified candidates based on specific criteria. It could streamline the recruitment process, allowing your team to focus on interviewing and evaluating the best matches for the role. However, if the AI system is trained on biased data, such as the notion that men dominate the finance industry or nurses are primarily female, it may unfairly prioritize candidates and overlook qualified ones from diverse backgrounds” (Harvard Business School, 2024). A well-known case at Amazon in 2014 demonstrates how its AI-powered hiring tool, trained predominantly on male applicants’ resumes, produced biased recommendations favouring men and leading Amazon to ultimately discard the tool. Apparently, the system effectively taught itself that male candidates were preferable (BBC, 2018).

The consequence of these ethical failure are significant to businesses, it can be classified as commercial risk, reputational risk and regulatory risk. AI system can fail in ways that can result in individuals can be unfairly treated by denying access  to services or experiencing systematically worse treatment based on protected attribute. For example, “a facial recognition system in the US misidentified a black teen excluding her from an ice-skating rink. Research shows that algorithms used in FRT are significantly less accurate when distinguishing between people of colour, women, and children.25 This could result in breaches of anti-discrimination laws” (Human Technology Institute, 2023, p. 17). According to the latest survey done by Forbes, two-thirds of board members and executives still have limited to no knowledge or experience with AI and though roughly half of the leaders surveyed said their organizations provide foundational AI education to their boards, which is an overall improvement; but it still means the other half are not (Forbes, 2025).

To conclude, businesses that implement ai without a strong ethical regulation contributes to algorithm bias, lack of privacy and accountability. The reliance of biased data inadequate ethics governance and insufficient investment in responsible ai research. Addressing this multifaceted ethical failure requires businesses to embed transparency, fairness, accountability and board-level ethical expertise into their AI ecosystems, supported by active engagement with responsible AI research and regulatory compliance.

Australian Government, Department of Industry, Science and Resources. (2025). AI adoption in Australian businesses for 2024 Q4. https://www.industry.gov.au/news/ai-adoption-australian-businesses-2024-q4#:~:text=The%20data%20reveals%20a%20positive,by%202%25%20to%2021%25.

BBC. (2018). Amazon scrapped 'sexist AI' tool. https://www.bbc.com/news/technology-45809919

Forbes. (2025). Governance And AI: Are Boards Keeping Up?. https://www.forbes.com/sites/deloitte/2025/08/18/governance-and-ai-are-boards-keeping-up/

ITBrief, Australia. (2023). 66% of Aussie organisations suffer from data and AI bias. https://itbrief.com.au/story/66-of-aussie-organisations-suffer-from-data-and-ai-bias#:~:text=62%25%20of%20Australian%20organisations%20anticipate,than%20any%20other%20countries%20surveyed

The Technology Institute. (2023). The State of AI Governance in Australia. https://www.uts.edu.au/globalassets/sites/default/files/2023-05/hti-the-state-of-ai-governance-in-australia---31-may-2023.pdf

Ladwig, C., Schwieger, D., Mitra, R., (2025). Countering the “Plagiarism Slot Machine”: Protecting Creators and Businesses from AI Copyright Infringement. Information Systems Education Journal 23(5) pp 53-61. https://doi.org/10.62273/PCXH9792

Harvard Business School Online. (2024). 5 Ethical Considerations of AI in Business. https://online.hbs.edu/blog/post/ethical-considerations-of-ai

One example of a black box ai system failing to meet expected standers was in 2025 when the commonwealth bank reversed its decision to eliminate numerous customer service roles. These roles were considered redundant due to the innovation and development of a new ai powered voice bot system. This bot was originally expected to reduce the workload and necessity for the common wealth bank to hire more individuals than necessary, however the ai chat bot was proven to be in inefficient replacement to the individuals originally working these customer service roles as after their deployment customer service calls and complaints increased which led to internal scrambling and resulted in management going into overtime to correct this problem as well as bringing back the recently made redundant team leaders to return to the phones after the clear failings of the ai chat bot. (Chalmers S, 2025). The lack of transparency of how the ai worked mean that stakeholders of the company had no insights as to how or why the ai failed in its duties or why the decisions were made.

One company working in Australia proactively addressing the ethical issues of transparency and accountability within the realm of implementing ai in Australian businesses is the APSC, the Australian Public Service Commission. The commission is dedicated to the safe, transparent, and ethical use of ai technologies that accords with evolving legislation ethical standers and expectations.  By implementing measures such as assessment preparation, monitoring, and adaptation to changes in ai policy requirements and technology, The Australian Public Service Commission seeks to create a public trust to ensure that companies implementing various ai technologies into their businesses are using these new technological advancements in a responsible, transparently, and ethical way across the country. As well as this the APSC safeguards against risks and ensures responsible ai usage through releasing an annual AI transparency statement the provides visibility on how the use of ai is managed. This proactive approach taken by the Australian Public Service Commission has set a precedent for other businesses in Australia in how they address and ensure that their use of ai across their own companies and organisations are following the guild lines set by the Australian AI Ethics Principles, especially in regards to transparency and accountability of how there AI systems actually function and how they will affect various job positions, shareholders and employees as well as their general consumers or customers.

Huang, F. (2019). Black box AI: Unveiling the risks of uninterpretable models. Unsw.edu.au. https://www.businessthink.unsw.edu.au/articles/black-box-AI-models-bias-interpretability

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‌Chalmers, S. (2025, August 21). Commonwealth Bank backtracks on AI job cuts, apologises for “error” as call volumes rise. Abc.net.au; ABC News. https://www.abc.net.au/news/2025-08-21/cba-backtracks-on-ai-job-cuts-as-chatbot-lifts-call-volumes/105679492

Transparent Use of AI at the Commission. (2025, March 5). Australian Public Service Commission. https://www.apsc.gov.au/about-us/accountability-and-reporting/transparent-use-ai-commission