AI needs us: Why human oversight is crucial for realising AI's potential
Human oversight: Exploring and validating AI
AI adoption in Asia Pacific trails behind the global average, with only 15% of organisations in the region fully prepared to deploy and harness AI. Infor's latest study, How Possible Happens, also reveals that only just over one-tenth of APAC organisations use digital technology to automate repetitive, low-value tasks. One barrier is the identification of AI use cases, with many decision makers struggling to distinguish between valuable and incidental applications.
To overcome this challenge, businesses need to tap into human knowledge and experience of AI. Viewing AI adoption as a dynamic, human-driven process tailored to specific business needs is key. After all, human judgement is critical for evaluating AI's value and identifying and prioritising applications based on organisational requirements.
The language surrounding AI integration should emphasise this human-centric approach. Specifically, AI should be portrayed as a tool that empowers employees. It should be seen as a means to augment human capabilities, requiring human guidance and input, rather than as a replacement for the workforce.
AI providers should proactively engage with customers to explore potential applications, initiating discussions, identifying client pain points, and demonstrating how AI solutions can effectively address these challenges.
Human direction: Driving efficiency and service innovation
The classic IT adage, 'garbage in, garbage out,' is particularly pertinent for AI. If the data used to train or operate a model is poor, the outcomes will be similarly lacking. Data scientists and engineers are crucial in ensuring data acquisition, cleansing and lifecycle management for AI applications. Their responsibilities include evaluating data quality, identifying appropriate datasets, and reviewing data governance practices. Should the data be substandard, AI experts may suggest improvements in data cleaning, structuring, or governance before proceeding.
Even in production environments, human oversight is crucial for directing and controlling AI applications. For example, in process optimisation, AI and machine learning can reveal insights that humans can leverage to enhance efficiency. This scenario is a reversal from the training phase: AI now provides the data, while humans process this information by interpreting and acting on it to refine processes, innovate services, and enhance decision-making. We are fast approaching a world where humans and AI agents collaborate seamlessly to boost efficiency, but with humans still very much in the driving seat.
Human responsibility: Maintaining and monitoring AI
Many AI projects struggle to advance beyond the proof-of-concept stage, often due to insufficient maintenance resources. This underlines the need for continuous human involvement, not only to prevent model decay but also to guarantee the long-term success, adoption and scalability of AI initiatives. Change management is also a crucial part of its success.
For instance, human oversight is indispensable to ensuring that AI models maintain their accuracy and relevance over time. This is especially vital in complex, evolving environments where data inputs and conditions can change.
Similarly, human experts play a crucial role in monitoring the performance of AI models by identifying and addressing emerging issues that could hinder functionality or accuracy.
Human context: European innovation and challenges
Although the majority of AI technologies used today are developed in the US, European researchers make a substantial contribution to their development. With the EU AI Act, the bloc is also a regulatory leader and playing a crucial role in ensuring the responsible deployment of AI technologies.
However, regulatory and administrative challenges can hinder innovation. In this context, human employees are crucial, with regulatory experts playing a vital role in helping businesses meet their compliance obligations while fostering an environment that supports AI development. Additionally, human oversight is essential to ensure the ethical development and deployment of AI, safeguarding both innovation and integrity.
Human-centric AI
AI holds great promise, but its models require care. Only with human oversight can this technology fulfil its potential to deliver the efficiencies promised. Furthermore, when humans maintain control, AI becomes a powerful tool that not only enhances their capabilities but also optimises workflows and fosters greater innovation.
Try Our AI Features
Explore what Daily8 AI can do for you:
Comments
No comments yet...
Related Articles
Techday NZ
10 hours ago
- Techday NZ
Sensitive data exposure rises with employee use of GenAI tools
Harmonic Security has released its quarterly analysis finding that a significant proportion of data shared with Generative AI (GenAI) tools and AI-enabled SaaS applications by employees contains sensitive information. The analysis was conducted on a dataset comprising 1 million prompts and 20,000 files submitted to 300 GenAI tools and AI-enabled SaaS applications between April and June. According to the findings, 22% of files (total 4,400) and 4.37% of prompts (total 43,700) included sensitive data. The categories of sensitive data encompassed source code, access credentials, proprietary algorithms, merger and acquisition (M&A) documents, customer or employee records, and internal financial information. Use of new GenAI tools The data highlights that in the second quarter alone, organisations on average saw employees begin using 23 previously unreported GenAI tools. This expanding variety of tools increases the administrative load on security teams, who are required to vet each tool to ensure it meets security standards. A notable proportion of AI tool use occurs through personal accounts, which may be unsanctioned or lack sufficient safeguards. Almost half (47.42%) of sensitive uploads to Perplexity were made via standard, non-enterprise accounts. The numbers were lower for other platforms, with 26.3% of sensitive data entering ChatGPT through personal accounts, and just 15% for Google Gemini. Data exposure by platform Analysis of sensitive prompts identified ChatGPT as the most common origin point in Q2, accounting for 72.6%, followed by Microsoft Copilot with 13.7%, Google Gemini at 5.0%, Claude at 2.5%, Poe at 2.1%, and Perplexity at 1.8%. Code leakage represented the most prevalent form of sensitive data exposure, particularly within ChatGPT, Claude, DeepSeek, and Baidu Chat. File uploads and risks The report found that, on average, organisations uploaded 1.32GB of files in the second quarter, with PDFs making up approximately half of all uploads. Of these files, 21.86% contained sensitive data. The concentration of sensitive information was higher in files compared to prompts. For example, files accounted for 79.7% of all stored credit card exposure incidents, 75.3% of customer profile leaks, and 68.8% of employee personally identifiable information (PII) incidents. Files accounted for 52.6% of exposure volume related to financial projections. Less visible sources of risk GenAI risk does not only arise from well-known chatbots. Increasingly, regular SaaS tools that integrate large language models (LLMs) - often without clear labelling as GenAI - are becoming sources of risk as they access and process sensitive information. Canva was reportedly used for documents containing legal strategy, M&A planning, and client data. Replit and were involved with proprietary code and access keys, while Grammarly and Quillbot edited contracts, client emails, and internal legal content. International exposure Use of Chinese GenAI applications was cited as a concern. The study found that 7.95% of employees in the average enterprise engaged with a Chinese GenAI tool, leading to 535 distinct sensitive exposure incidents. Within these, 32.8% were related to source code, access credentials, or proprietary algorithms, 18.2% involved M&A documents and investment models, 17.8% exposed customer or employee PII, and 14.4% contained internal financial data. Preventative measures "The good news for Harmonic Security customers is that this sensitive customer data, personally identifiable information (PII), and proprietary file contents never actually left any customer tenant, it was prevented from doing so. But had organizations not had browser based protection in place, sensitive information could have ended up training a model, or worse, in the hands of a foreign state. AI is now embedded in the very tools employees rely on every day and in many cases, employees have little knowledge they are exposing business data." Harmonic Security Chief Executive Officer and Co-founder Alastair Paterson made this statement, referencing the protections offered to their customers and the wider risks posed by the pervasive nature of embedded AI within workplace tools. Harmonic Security advises enterprises to seek visibility into all tool usage – including tools available on free tiers and those with embedded AI – to monitor the types of data being entered into GenAI systems and to enforce context-aware controls at the data level. The recent analysis utilised the Harmonic Security Browser Extension, which records usage across SaaS and GenAI platforms and sanitises the information for aggregate study. Only anonymised and aggregated data from customer environments was used in the analysis.
Scoop
15 hours ago
- Scoop
Statement On AI In Universities From Aotearoa Communication & Media Scholars Network
We speak as a network of Aotearoa academics working in the inter-disciplines of Communication and Media Studies across our universities. Among us we have shared expertise in the political, social and economic impacts of commercially distributed and circulated generative artificial intelligence ('AI') in our university workplaces. While there is a tendency in our universities to be resigned to AI as an unstoppable and unquestionable technological force, our aim is to level the playing field to promote open critical and democratic debate. With this in mind, we make the following points: For universities… · AI is not an inevitable technological development which must be incorporated into higher education; rather it is the result of particular techno-capitalist ventures, a context which needs to be recognised and considered; · AI, as a corporate product of private companies such as OpenAI, Google, etc., encroaches on the public role of the university and its role as critic and conscience, and marginalises voices which might critique business interests; For researchers… · AI impedes rather than supports productive intellectual work because it erodes important critical thinking skills; instead, it devolves human scholarly work and critical engagement with ideas–elements vital to our cultural and social life–to software that produces 'ready-made', formulaic and backward looking 'results' that do not advance knowledge; · AI promotes an unethical, reckless approach to research which can promote 'hallucinations' and over valorise disruption for its own sake rather than support quality research; · AI normalises industrial scale theft of intellectual property as our written work is fed into AI datasets largely without citation or compensation; · AI limits the productivity of academic staff by requiring them to invent new forms of assessment which subvert AI, police students and their use of AI, or assess lengthy 'chat logs', rather than engage with students in activities and assessments that require deep, critical thinking and sharing, questioning and articulating ideas with peers; For students… · AI tools create anxiety for students; some are falsely-accused of using generative-AI when they haven't, or are very stressed that it could happen to them; · AI tools such as ChatGPT are contributing to mental-health crises and delusions in various ways; promoting the use of generative-AI in academic contexts is thus unethical, particularly when considering students and the role of universities in pastoral care; · AI thus undermines the fundamental relationships between teacher and student, academics and administration, and the university and the community by fostering an environment of distrust; For Aotearoa New Zealand… · AI clashes with Te Tiriti obligations around data sovereignty and threatens the possibility of data colonialism regarding te reo itself; · AI is devastating for the environment in terms of energy and water use and the extraction of natural resources needed for the processors that AI requires. Signed by: Rosemary Overell, Senior Lecturer, Media, Film & Communications Programme, The University of Otago Olivier Jutel, Lecturer, Media, Film & Communications Programme, The University of Otago Emma Tennent, Senior Lecturer, Media & Communication, Te Herenga Waka Victoria University of Wellington Rachel Billington, Lecturer, Media, Film & Communications Programme, The University of Otago Brett Nicholls, Senior Lecturer, Media, Film & Communications Programme, The University of Otago Yuki Watanabe, Lecturer, Media, Film & Communications Programme, The University of Otago Sy Taffel, Senior Lecturer, Media Studies Programme, Massey University Leon Salter, Senior Lecturer, Communications Programme, University of Auckland Angela Feekery, Senior Lecturer, Communications Programme, Massey University Ian Huffer, Senior Lecturer, Media Studies Programme, Massey University Pansy Duncan, Senior Lecturer, Media Studies Programme, Massey University Kevin Veale, Senior Lecturer, Media Studies Programme, Massey University Peter A. Thompson, Associate Professor, Media & Communication Programme, Te Herenga Waka/Victoria University of Wellington Nicholas Holm, Associate Professor, Media Studies Programme, Massey University Sean Phelan, Associate Professor, Massey University Yuan Gong, Senior Lecturer, Media Studies Programme, Massey University Chris McMillan, Teaching Fellow, Sociology Programme, University of Auckland Cherie Lacey, Researcher, Centre for Addiction Research, University of Auckland Thierry Jutel, Associate Professor, Film, Te Herenga Waka, Victoria University of Wellington Max Soar, Teaching Fellow, Political Communication, Te Herenga Waka Victoria University of Wellington Lewis Rarm, Lecturer, Media and Communication, Te Herenga Waka | Victoria University of Wellington Tim Groves, Senior Lecturer, Film. Te Herenga Waka, Victoria University of Wellington Valerie Cooper, Lecturer, Media and Communication, Te Herenga Waka | Victoria University of Wellington Wayne Hope, Professor, Faculty of Design & Creative Technologies, Auckland University of Technology Greg Treadwell, senior lecturer in journalism, School of Communication Studies, Auckland University of Technology Christina Vogels, Senior Lecturer, Critical Media Studies, School of Communication Studies, Auckland University of Technology
RNZ News
a day ago
- RNZ News
Amazon profits surge 35% as AI investments drive growth
By AFP Despite the stellar results, investors seemed worried about Amazon's big cash outlays to pursue its AI ambitions. Photo: 123RF Amazon has reported a 35 percent jump in quarterly profits as the e-commerce giant says major investments in artificial intelligence has been paying off. The Seattle-based company posted net profit of $18.2 billion (NZ$30.9 billion) for the second quarter that ended June 30, compared with $13.5 billion (NZ$22.9 billion) in the same period last year. Net sales climbed 13 percent to $167.7 billion (NZ$284.7 billion), beating analyst expectations and signalling that the global company was surviving the impacts of the high-tariff trade policy under US President Donald Trump. "Our conviction that AI will change every customer experience is starting to play out," chief executive Andy Jassy said, pointing to the company's expanded Alexa+ service and new AI shopping agents. Amazon Web Services (AWS), the company's world leading cloud computing division, led the charge with sales jumping 17.5 percent to $30.9 billion (NZ$52.45 billion). The unit's operating profit rose to $10.2 billion (NZ$17.3 billion) from $9.3 billion (NZ$15.8 billion) a year earlier. The strong AWS performance reflects surging demand for cloud infrastructure to power AI applications, a trend that has benefited major cloud providers as companies race to adopt generative AI technologies. Despite the stellar results, investors seemed worried about Amazon's big cash outlays to pursue its AI ambitions, sending its share price more than three percent lower in after-hours trading. The company's free cash flow declined sharply to $18.2 billion (NZ$30.9 billion) for the trailing 12 months, down from $53 billion (NZ$90 billion) in the same period last year, as Amazon ramped up capital spending on AI infrastructure and logistics. The company spent $32.2 billion (NZ$54.7 billion) on property and equipment in the quarter, nearly double the $17.6 billion (NZ$29.9 billion) spent a year earlier, reflecting massive investments in data centres and backroom capabilities. Amazon has pledged to spend up to $100 billion (NZ$169.8 billion) this year, largely on AI-related investments for AWS. For the current quarter, Amazon forecast net sales between $174.0 billion (NZ$295 billion) and $179.5 billion (NZ$304.8 billion), representing solid growth of 10-13 percent compared with the third quarter of 2024. Operating profit was expected to range from $15.5 billion (NZ$26.3 billion) to $20.5 billion (NZ$34.8 billion) in the current third quarter, which was lower than some had hoped for and likely also a factor in investor disappointment. - AFP
