for an essay that answers the following: “You are a computer graduate about to start a job with an AI company involved in the development and deployment of AI applications (like Chat GPT) for use by students. As a recent student yourself, give a unique perspective (with personal insights and anecdotes) supported by academic references to discuss the ethical considerations needed to be reflected on by the developers during this job.” This being the answer to the essay: “a. Being a novice graduate primed and readying to venture forth into the realm of AI development, specifically a leading- edge organisation which curates AI applications akin to ChatGPT purported for educational enhancement; with an insightful awareness, I recognise and am deeply attuned to the ethical considerations woven into the very fabric of my professional endeavours. Via the integration of personal experiences accompanied by scholarly insight, it is my aim to elucidate the primary ethical considerations that necessitate deliberation by respective developers to warrant that instruments such as these augment the educational continuum propitiously to pose as a mode of pedagogical amelioration as opposed to a regressive dependency.

The assimilation of AI technologies across the educational spectrum has evoked acclaim and criticism alike solely for its potency to precipitate a paradigmatic shift in educational practices. The utilisation of generative AI technologies such as ChatGPT elevates the educational landscape through offering nuanced and bespoke learning assistance, tailoring to the individualistic requirements of each and every student (Holstein, et al., 2017). This adaptation to individual learning needs, emanating from personal experiences, I found notably results in an upsurge in learner engagement whilst fortifying the longevity of knowledge retention. Nevertheless, as (Buchanan, et al., 2018) cautions, it is imperative that this technological advancement is subjected to rigorous ethical scrutiny to avoid compromising and maintain the sanctity of educational standards. There exists a very thin line between what may be deemed as assistance, and conversely what may be considered cheating; the employment of AI generative tools contributes to the obscurity of this distinction (Selwyn, 2021). It is imperative in designing systems that foster and supplement the learner’s journey to true content comprehension, in place of those that exclusively serves as answer repositories as I myself have firsthand witnessed peers diverge into ethically dubious territory where the deployment of AI was commonplace for assignment completion and for gaining an underhanded advantage over others. Even so, throughout the duration as my tenure as a student, I meticulously employed analogous instruments in my academic pursuits, not to shortcut my learning process but instead as a facilitator of deeper understanding, and to orchestrate my problem solving aptitude.

The primacy of the all-encompassing conservation of data privacy stands as a vital and indispensable pillar in the ethical framework that governs the pursuits of artificial intelligence developers. The advent of artificial intelligence in educational contexts comprises an elaborate and systematic process of harvesting, assessing, and the preservation of voluminous quantities of student- associated data, amongst which resides information of a sensitive and personally identifiable nature. Developers of artificial intelligence systems bear a fiduciary responsibility, a natural progression of the unimpeachable teacher- student trust borne covenant, to manage data with the highest degree of diligence and reverence that is untainted by intrusive surveillance; this, as a bare minimum are what students should be entitled to (Debe, 2021). This not only moral, yet an obligation that is further underscored by legislative apparatuses like the General Data Protection Regulation (GDPR) within the European Union (EU), which delineates stringent criteria for the processing and safeguarding of data (Voigt & Von dem Bussche, 2017). Within the domain of educational artificial intelligence, data constitutes the vital essence that fuels the sophisticated customisation and agility which render platforms such as ChatGPT profoundly transformative. Regardless of this, this heavy dependency on data presents an elaborate ethical tapestry intertwined with trepidations with regards to student privacy, consent and not to mention the potentiality for the misuse of user data, sensitive or not (Zook, et al., 2017). In retrospect, my academic journey was punctuated by seamless exchanges with technological instrumentation and interfaces, interactions that called into question the subtle data interchanges unfolding in the digital ether; interchanges of which now form the foundation of my prospective professional duties.

Pertaining to the ambit of artificial intelligence, ‘algorithmic bias’ is characterised by chronic aberrations that engender outcomes lacking in impartiality; this typically is indictive of ingrained distortions that foment consistently biased outcomes, frequently bestowing an underserved advantage upon a distinct faction of users purely selected by sheer chance (Friedman & Nissenbaum, 1996). The recurrent theme of algorithmic bias across academic research and discussions accentuates a fundamental mandate for developers: to both discern and ameliorate biases to veer of the course of the entrenchment of detrimental stereotypes or ‘biases’ contained by artificial intelligence tools (Benjamin, 2019). In light of such realisation , it is clearer than ever that as developers, our duty calls for the ceaseless and incumbent vicious cycle of revaluation and refinery for these modes of generative artificial intelligence to consistently guarantee and exemplify equity, whilst we imbue our work with an acute awareness of the diverse linguistic and cultural fabric that constitutes the student body; thereby ascribing impartial representation within the algorithm’s evaluative framework (Buolamwini & Gebru, 2018). Artificial intelligence technology, whilst pioneering in its conceptualisation, it carries with it the risk of prejudicial inclinations derived from a range of sources inclusive of the underlying structures of society reflected in the training data, and its foundational training datasets (which may be subject to unconscious dependent developer preferences)- propensities that may inadvertently perpetuate social inequities and stratifications (Dingum, 2018; Barocas & Selbst, 2016). My academic journey and subsequent foray into the realm of professionalism have all the more illuminated the increasingly critical necessity of the integration of heterogeneous datasets alongside capturing a varied cadre of ample stakeholder insights to champion fairness and inhibit bias escalation in the practical applications of artificial intelligence (Zou & Schiebinger, 2018).” Please can you write a short and concise array of conclusions consistent of 100 words at most ensuring to be eloquent, professional and gramatically impressive as you can.