R E G I O N A L C H E C K - U P activity patterns tied to specific attention states – their functionality would see a significant boost . What ’ s more , neural devices linked to the Internet could open the possibility for individuals or entities ( like hackers , corporations or governmental agencies ) to observe or even influence an individual ’ s mind .

Research findings from adaptive deep brain stimulation ( aDBS ) experts suggest that although the use of aDBS systems for enhancement might be a distant reality , it ’ s not an impossible one , with a substantial majority ( 70 %) acknowledging its potential . Simultaneously , these specialists have expressed deep ethical concerns related to safety and security , the perception of enhancement as unnecessary or unnatural , and matters of fairness , equality and distributive justice .

We must develop robust ethical frameworks grounded in respect for autonomy , beneficence , nonmaleficence and justice . These principles can guide us in creating safeguards to ensure that AI technologies used in neural implants are designed and used ethically . To limit this problem , we propose that the sale , commercial transfer and use of neural data be strictly regulated . Such regulations , which would also limit the possibility of people giving up their neural data or having neural activity written directly into their brains for financial reward , may be linked to existing legislation that prohibits the sale of human organs , such as the 1984 US National Organ Transplant Act .

Intellectual property rights

The convergence of AI and neural implants presents unique complexities within the current intellectual property ( IP ) law framework . Consider , for example , the predicament that arises when a company that provides a neural implant solution declares bankruptcy . Does the user , whose life quality has been considerably enhanced by the implant , retain the product ? Or does the firm , the holder of the IP rights , possess the power to retrieve it ?

Traditional IP laws were designed when only human inventors were considered . This presents a multitude of questions and challenges in the era of escalating reliance on AI systems , whether they contribute to an invention or create one outright .

As neurotechnology merges with AI , the classical definition of an inventor as a ‘ natural person ’ becomes insufficient . Can an AI be classified as an inventor , particularly when it contributes significantly to the creation of a neural device or technique ? Different jurisdictions have varying definitions of inventorship . In the US , an inventor must be a natural person , while other jurisdictions reference the Paris Convention for the Protection of Industrial Property , which mandates that an inventor be human . However , the Paris Convention only specifies the right of an inventor to be acknowledged as such in the patent . So , could this right be extended to an AI system ?

Another issue is disclosure requirements . AI innovations are often the result of black box operations by the machine , which makes it impossible to disclose the innovations in sufficient levels of detail to satisfy existing laws . Patents , copyright and trademarks may not be enough to protect an AI-related invention .

The development of neurotechnology also frequently involves collaboration between AI developers , neuroscientists and biomedical engineers . Current IP laws may not adequately address such collaborative innovation , potentially leading to disputes .

This area of uncertainty demands comprehensive policy discussions and international convergence . It ’ s a balancing act ; on one side , inventors ’ IP rights must be protected to encourage innovation . Conversely , users ’ rights to health and well-being must be preserved . This context of uncertainty should also be seen as a chance to explore new rules for IP protection . For example , policymakers can reconsider the definition of an ‘ inventor ’, incorporate ethical aspects directly into the IP rights and under certain circumstances , adjust patent laws to include provisions for mandatory licensing .

The broader global policy landscape and digital divide

It ’ s crucial to recognise that the technology gap is likely to expand further as developed countries – the frontrunners in AI – extend their lead in its adoption and use . Back in 2018 , forecasts for 2030 suggested that AI systems could make up 20 – 25 % of the overall economic gain in developed countries , as opposed to a 5 – 15 % contribution in developing nations . Considering the trend identified in McKinsey ’ s study , it ’ s not hard to surmise that this disparity has likely grown even further since then .

It ’ s widely understood that when scientific or technological choices are rooted in a limited range of systemic , structural or societal norms and ideas , the ensuing technology is capable of favouring specific groups while disadvantaging others . Hence , the question arises : how effective are the present-day regulations and laws in developed countries at addressing bias ?

Although it ’ s hard to pinpoint where bias has crept into AI models today , based on DataRobot ’ s 2022 State of AI Bias Report , more than one in three organisations surveyed ( mostly multinational CIOs , IT directors , managers , data scientists and development leads ) have experienced challenges or direct business impact

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