The continuous interest in the field of Artificial Intelligence AI has been the main reason for the constant progress in this field and its improvements and the transition with qualitative steps from simple learning that requires a lot of effort and time to deep learning models and self-learning models that led to benefit from this field with all its capabilities for social good. Many successful AI factors are needed for good social usage, like preventing falsifiability, data protection, Situational fairness, Human-Friendly Semanticisation, etc. (Floridi, 2020).
All the capabilities and developments of AI must be at the service of the human process first, in the way that it is safe and not harmful to the environmental environment and always benefits in the long term and the optimal goal, and most importantly, the living organisms that must be carefully and carefully considered to harness this field to serve them (Shaping Europe’s digital future, 2019).
In order to safely exploit artificial intelligence, it was necessary to search for the best methods for this, especially since human trials must be subject to close scrutiny. Therefore it was essential to send a survey and quantitative analysis to all those registered in any experimental step with recording all the detailed notes and in a way that reaches an experimental model, trustworthy to carry out the practical application of industrial techniques with guidelines, ethical controls and self-assessment processes for these applications (Vincent, 2019).
Best AI Applications
Different distinctive AI applications are now available in all fields, such as machine translation (Google translator), big data analysis (deep learning for manipulation of large image dataset like Flickr and google), decision support systems, especially in the medical field, virtual assistant (like Siri and Alexa which can be used for multiple purposes such as setting alarms, suggesting a film-watch list, reminding appointments, querying about the weather, suggesting the best restaurants, etc.), education AI application, scene understanding and image captioning algorithms used by many platforms like Facebook and Twitter, face and speech recognition application, etc. (Useche, 2019).
Through a set of smart algorithms based on humans’ thinking process, AI can reach a similar result to a human think when given the same information. All this falls within the framework of supporting neural networks to provide virtual services that contribute to more sophistication.
Ethical controls for using artificial intelligence (GELMAN, 2019):
These ethical controls address a serious problem in the field of the application of artificial intelligence techniques in the lives of individuals. AI continues to improve the human reality as a whole, and to achieve this, AI must have barriers that prevent it from restricting human freedom, and as it achieves after all that accuracy, durability and security, especially since the issue here is the lives of individuals, their safety and their personal information, which should not at any moment be subject to theft or breach of privacy. All of this must fall under the concept of transparency and ease in taking advantage of these important services provided by AI (Marcus, 2017). For example, the deep fake is one of the bad usages of AI that can be used to create virtual fake images and videos of somebody or even create a virtual fake human.
Preventing the technological exploitation of artificial intelligence:
The human field and its advancement is the first thing that any company with profit-oriented goals thinks about. In the event that AI has a significant impact in the future in technological progress, all of this must be controlled so that it is not profitable and must be subject to control and accountability standards that make it protected from everyone who thinks to politicize its work.
All of these controls must consider digital privacy and the freedom to benefit from artificial intelligence for purposes that serve humanity. Still, the form in which these control methods must be pursued must be effective in a way that does not always depend on censorship (State for Digital, Culture, Media & Sport and the Secretary of State for the Home Department, 2019). The primary reliance on the immunization of artificial intelligence and its uses in a purposeful, protected, and accessible manner to everyone without any harm may result from it (Ballarchive, 2019).
Questions to be analyzed and focus on
Many important questions should be introduced in AI, like who is creating AI systems and why they are created for? Who can control these AI applications? Can we create AI useful applications, but bad usage of them is possible? Can deep-learning algorithms produce deep-learning students? And how can we get useful results from data? Are the virtual assistants totally safe so that my data cannot be accessed by anyone else? Is my cloud data secure and cannot be violated by others in the cloud? (Useche, 2019).
Ballarchive, J. (2019, 4 8). The UK’s online laws could be the future of the internet—and that’s got people worried. Retrieved from technology review: https://www.technologyreview.com/2019/04/08/136157/the-uks-online-laws-could-be-the-future-of-the-internetand-thats-got-people-worried/
Floridi, L. (2020, 4 3). How to Design AI for Social Good: Seven Essential Factors. Science and Engineering Ethics, pp. 1771–1796.
GELMAN, A. (2019, 4 3). From Overconfidence in Research to Over Certainty in Policy Analysis: Can We Escape the Cycle of Hype and Disappointment? Retrieved from Shorenstein centre: https://ai.shorensteincenter.org/ideas/2019/4/3/from-overconfidence-in-research-to-over-certainty-in-policy-analysis-can-we-escape-the-cycle-of-hype-and-disappointment
Marcus, G. (2017). Deep Learning: A Critical Appraisal. New York: New York University.
Shaping Europe’s digital future. (2019, 4 8). Ethics guidelines for trustworthy AI. Retrieved from Europa: https://ec.europa.eu/digital-single-market/en/news/ethics-guidelines-trustworthy-ai
Solon, O. (2019). facial recognition dirty little secret. Retrieved from https://www.nbcnews.com/tech/internet/facial-recognition-s-dirty-little-secret-millions-online-photos-scraped-n981921
State for Digital, Culture, Media & Sport and the Secretary of State for the Home Department. (2019). Online Harms White Paper. UK: APS Group.
Useche, D. O. (2019). CCTP-607: “Big Ideas”: AI to the Cloud. Retrieved from Georgetown: https://blogs.commons.georgetown.edu/cctp-607-spring2019/category/week-12/
Vincent, J. (2019). AI systems should be accountable, explainable, and unbiased, says EU. Retrieved from Theverge.com: https://www.theverge.com/2019/4/8/18300149/eu-artificial-intelligence-ai-ethical-guidelines-recommendations