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Artificial intelligence (AI) was once the stuff of science fiction. But its becoming widespread. It is used in mobile phone technology and motor vehicles. It powers tools for agriculture and healthcare.

But concerns have emerged about the accountability of AI and related technologies like machine learning. In December 2020 a computer scientist, Timnit Gebru, was fired from Googles Ethical AI team. She had previously raised the alarm about the social effects of bias in AI technologies. For instance, in a 2018 paper Gebru and another researcher, Joy Buolamwini, had showed how facial recognition software was less accurate in identifying women and people of colour than white men. Biases in training data can have far-reaching and unintended effects.

There is already a substantial body of research about ethics in AI. This highlights the importance of principles to ensure technologies do not simply worsen biases or even introduce new social harms. As the UNESCO draft recommendation on the ethics of AI states:

We need international and national policies and regulatory frameworks to ensure that these emerging technologies benefit humanity as a whole.

In recent years, many frameworks and guidelines have been created that identify objectives and priorities for ethical AI.

This is certainly a step in the right direction. But its also critical to look beyond technical solutions when addressing issues of bias or inclusivity. Biases can enter at the level of who frames the objectives and balances the priorities.

In a recent paper, we argue that inclusivity and diversity also need to be at the level of identifying values and defining frameworks of what counts as ethical AI in the first place. This is especially pertinent when considering the growth of AI research and machine learning across the African continent.

Research and development of AI and machine learning technologies is growing in African countries. Programmes such as Data Science Africa, Data Science Nigeria, and the Deep Learning Indaba with its satellite IndabaX events, which have so far been held in 27 different African countries, illustrate the interest and human investment in the fields.

The potential of AI and related technologies to promote opportunities for growth, development and democratisation in Africa is a key driver of this research.

Yet very few African voices have so far been involved in the international ethical frameworks that aim to guide the research. This might not be a problem if the principles and values in those frameworks have universal application. But its not clear that they do.

For instance, the European AI4People framework offers a synthesis of six other ethical frameworks. It identifies respect for autonomy as one of its key principles. This principle has been criticised within the applied ethical field of bioethics. It is seen as failing to do justice to the communitarian values common across Africa. These focus less on the individual and more on community, even requiring that exceptions are made to upholding such a principle to allow for effective interventions.

Challenges like these or even acknowledgement that there could be such challenges are largely absent from the discussions and frameworks for ethical AI.

Just like training data can entrench existing inequalities and injustices, so can failing to recognise the possibility of diverse sets of values that can vary across social, cultural and political contexts.

In addition, failing to take into account social, cultural and political contexts can mean that even a seemingly perfect ethical technical solution can be ineffective or misguided once implemented.

For machine learning to be effective at making useful predictions, any learning system needs access to training data. This involves samples of the data of interest: inputs in the form of multiple features or measurements, and outputs which are the labels scientists want to predict. In most cases, both these features and labels require human knowledge of the problem. But a failure to correctly account for the local context could result in underperforming systems.

For example, mobile phone call records have been used to estimate population sizes before and after disasters. However, vulnerable populations are less likely to have access to mobile devices. So, this kind of approach could yield results that arent useful.

Similarly, computer vision technologies for identifying different kinds of structures in an area will likely underperform where different construction materials are used. In both of these cases, as we and other colleagues discuss in another recent paper, not accounting for regional differences may have profound effects on anything from the delivery of disaster aid, to the performance of autonomous systems.

AI technologies must not simply worsen or incorporate the problematic aspects of current human societies.

Being sensitive to and inclusive of different contexts is vital for designing effective technical solutions. It is equally important not to assume that values are universal. Those developing AI need to start including people of different backgrounds: not just in the technical aspects of designing data sets and the like but also in defining the values that can be called upon to frame and set objectives and priorities.

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Defining what's ethical in artificial intelligence needs input from Africans - The Conversation Africa

The McGill Artificial Intelligence Society (MAIS) held its first in-person event of the school year, a panel titled Ethics in AI, on Nov. 17. The audience was at full capacity, drawing in a crowd of approximately 35 people from the McGill community to the Trottier lecture hall.

The panel featured three professionals who engage with issues surrounding AI ethics in their respective disciplines: Masa Sweidan, McGill alumna and business development manager at the Montreal AI Ethics institute (MAIEI); Ignacio Cofone, assistant professor of privacy law, AI law, and business associations at McGill; and Mark Likhten, legal innovation lead at Cyberjustice Lab at LUniversite de Montreal (UdeM).

Kaustav Das Sharma, U4 Engineering and team lead of the McGill AI Podcast, moderated the event. The panellists acknowledged that AI is often misrepresented in media and popular culture, and agreed that it is important for the public to gain a more holistic understanding of AI and the ethical barriers that emerge with its advancement.

What is important is to [] be clear about what AI is, Likhten said. It is a very powerful tool, but its still a tool which needs [] human intervention.

Cofone considered more precise issues, namely bias, transparency, and privacy within AI, as the issues that should garner more public attention. These three issues are at the core of his research in AI regulation.

One important aspect to be aware of [] is AI bias, Cofone said. AI decision-making affects everyone, everyday [.] Transparency [in AI] is important particularly with decision-making processes such as calculating credit scores to see if you would get a house [or] calculating your risk score to see if you go to jail [.] Privacy is important because most AI is trained with [sensitive] information about us.

There was also discussion regarding how public institutions can work to push inclusivity and diversity to the forefront of AI research and development. Sweidan stated that diversifying AI education is a crucial first step.

Having education that includes women, BIPOC, and LGBTQ [communities] is extremely important, Sweidan said. Having people with different backgrounds, looking at it from the philosophy standpoint, [from computer science], from law, I think that is what leads to a more holistic education, and I think that is an extremely important first step.

Panellists also discussed the potential of AI systems to inflict harm, and the importance of adequate personal data protection regulation. Ending on a positive note, Das Sharma asked panellists what makes them excited for a future blossoming with cutting-edge AI development. Sweidan said that she is excited by the possibilities for AI creativity, while Likhten cited the applications of AI in justice.

I am actually very optimistic for the future, said Likhten. [Cyberjustice Lab] works a lot with tools [using] AI to improve access to justice, and the possibilities that we see in that field are endless [.] We talk a lot about people getting stripped of their personal data [], and the bad sides of AI, but there are lots, and lots of [] good things that you can do with AI that remain within the boundaries of ethical principles.

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McGill Artificial Intelligence Society's panel discusses ethics and regulation of artificial intelligence - McGill Tribune

Data science and artificial intelligence are commonly used interchangeably in todays digital environment, but theyre not the same thing. AI and Data Science are two skill-based career pathways that cross frequently. With rich salaries and satisfying rewards, data science and artificial intelligence are the rocket ships blasting off in the post-pandemic period.By 2025, the worldwide data science industry is expected to be worth more than USD 178 billion. Due to their comparable skill sets, the phrases data scientist and artificial intelligence engineer are frequently interchanged. They are not, however, interchangeable in terms of functionality.

Data science and artificial intelligence are attractive professional options in the technology field. Data science and artificial intelligence (AI) have been hailed as promising occupations in the tech industry. If you want to work in the tech industry, you might look into different data analysis parts to determine which one appeals to you the most.Lets take it a step further and talk about data science and AI careers without wasting any time. These are two of the most popular and sought-after technologies, each with its own set of concepts and uses. Well assist you with your decision-making process in this post. The distinctions between data science and artificial intelligence will be discussed. This will help you gain a better grasp of the two and decide which one to pursue as a career.

Artificial intelligence engineer

Artificial intelligence is no longer a distant memory but has become a more integral part of our daily lives. Artificial intelligence engineers are the one-man army imparting human intelligence to machines, from building a robot hand for solving Rubiks cube to speech recognition systems. AI is all around us, from getting groceries delivered to asking Alexa to play your favourite music. An artificial intelligence engineer is in charge of creating clever autonomous models and integrating them into the software.

An AI engineer helps construct models for AI-based applications using machine learning techniques such as neural networks. These engineers have technically created AI-based specific systems for different purposes like language translation, some picture identification, and sentiment-based contextual advertising, to name a few. They collaborate with business stakeholders to build AI solutions that can assist businesses to enhance operations, service delivery, and product creation.

Organizations are starting to see the full impact of AI and machine learning on their operations. Some organizations artificial intelligence engineers are more research-oriented, focusing on discovering the best model for a task while training, monitoring, and deploying AI systems in production. The majority of business analysts are upskilling and changing careers to become citizen data scientists. A company must be able to integrate AI into its applications to become completely AI-driven.

To ensure that business goals are matched with the analytics back end, AI developers engage with business analysts, data scientists, and architects. This allows everyone in the organization to obtain access to the information they need to make better decisions. More artificial intelligence engineer opportunities are being created for people who can handle data science, software development, and hybrid data engineering works.

Data Scientist

A data scientist is a unicorn who uses algorithms, math, statistics, design, engineering, communication, and management capabilities to somehow extract the particular meaningful, worthwhile and actionable insights from specific massive amounts of data and positively influence the organization. On the other hand, data scientists analyze databases and extract meaningful insights for future forecasts using technologies like big data analytics, cloud computing, and ML. To extract insights from data, data scientists employ statistical methodologies, distributed architecture, visualization tools, and various data-oriented technologies such as Hadoop, Spark, Python, SQL, and R.

Simply saying, data science is quite impossible and incomplete without AI. Data scientists use the information they collect to drive various business operations, analyze user metrics, identify potential business hazards, assess market trends, and make smarter decisions to achieve organizational goals. A data scientist uses machine learning and predictive analytics to cope with exceedingly vast and complex datasets.When it comes to establishing a successful business, both AI and data science have a unique role. The ability to design algorithms that enable the collection and cleaning of such a large amount of data and prepare it for analysis is crucial. As a result, if businesses want to compete with future employment, theyll need both AI and data science.

Responsibilities and roles

Engineer in Artificial Intelligence

An artificial intelligence engineer collaborates with the data science team to originate, develop, and deliver production-ready AI products for enhanced business processes. Fake intelligence developers are given various organizational duties in addition to developing techniques.

Theyre in charge of designing and developing computer vision solutions that take advantage of machine learning and deep learning. Convert artificial intelligence and machine learning models into application programme interfaces (APIs) to be used by other programmes. Using object tracking algorithms, instance segmentation, semantic, object detection, and keypoint detection create scalable algorithms. Assist stakeholders in comprehending the output. Machine learning techniques such as zero-shot learning, GANs, few-shot learning, and self-supervised procedures are used. Set up and manage AI product infrastructure and the automation of the data science teams infrastructure. To construct deployable versions of the model, we used Docker technologies. Conduct statistical analysis and interpretation to assist organizations in making data-driven decisions. Methods for testing and deployment Developing and deploying functional, clever AI algorithms.

Data Scientist

The majority of a data scientists day focuses on data. A prominent data engineer is responsible for the whole data management and processing systems design, development, construction, installation, testing, and maintenance. Data scientists may find themselves engulfed in these responsibilities, ranging from data collection to data analysis and transformation. Their primary responsibility is to locate raw data and make it accessible to other specialists.

They must identify business or engineering-related challenges, transform them into data science problems, locate sources, analyze data, and develop a solution. The company will be unable to collect data from diverse sources without the assistance of a prominent data engineer.

Integrate several programming languages and technologies to create a comprehensive solution. Using a variety of techniques and technologies, deliver end-to-end analytical solutions. To manage large amounts of data, create systems that are highly scalable, robust, and fault-tolerant. In-depth, hands-on expertise in research or corporate environment with machine learning, data mining, statistical modelling, and unstructured data analytics. To stay ahead of the competition, new big data management tools and technologies must be introduced. Demonstrating expertise in classification methods, neural networks, cluster analysis, Bayesian modelling, and stochastic modelling, among other topics. Examine alternative data collecting options and experiment with innovative ways to use existing data.

Data Scientist vs Artificial Intelligence Engineer

The primary and particular job of a Data Engineer is to design and engineer a reliable and good infrastructure for transforming specific data into such particular formats as can be used by Data Scientists. Apart from building scalable pipelines to convert semi-structured and unstructured data into usable forms, Data Engineers must also identify meaningful trends in large datasets. Companies offering these generous salaries recognize the power of big data and are eager to use it to boost business decisions.Essentially, Data Engineers likewise work to prepare and also make raw data more and more useful as well as worthy for analytical or operational uses. Salaries for data scientists and artificial intelligence engineers are quite heading skyward, and these vary based on representing skills, their experience level in the field, and the companies hiring as well. The average salary of a good and average data scientist is approximately $117,543 per year, to be guessed.However, due to the increasing demand and trending era for skilled data scientists and artificial intelligence engineers, the salaries for these specific professionals are always changing. What makes the job of artificial intelligence engineers is that they produce autonomous and intelligent models. Even starting salaries in this job line are looking so increasingly attractive in this day today growing field.

Deploying Artificial Intelligence solutions is their sole responsibility as well. They should also get to know about distributed computing as Artificial Intelligence engineers work with very large amounts of data that technically cannot be stored on a single machine.

As career opportunities for AI engineers rapidly expand, AI engineers salaries will continue to climb. Salary ranges from 30 lakhs per annum for a data scientist to 50 lakhs per annum for an artificial intelligence engineer, depending on seniority. For AI engineers, a better understanding of the human thought process is a must-have ability.

Which Profession Should You Pursue?

There is a long list of careers available that integrate data science and artificial intelligence. An artificial intelligence engineer helps businesses build novel products that bring autonomy, while a data scientist creates data products that foster profitable business decision making.

Jobs like AI data analysts, prominent data engineers require a combined knowledge of data science and artificial intelligence. AI engineers and data scientists work together closely to create usable products for clients.

The primary responsibility of an AI data analyst includes procuring, preparing, cleaning, and modelling data using machine learning models and new analytical methods. In addition, the AI data analyst is in charge of designing and developing data reports that will assist stakeholders in making better decisions. An AI data analysts compensation ranges from 2.5 to 7.3 lakh rupees around counted on average.

Prominent data engineers are skilled as software developers, and they have to be proficient in coding, excellent data scientist, and engineer all at the same time. Both data scientists and AI developers stay up to date on the latest innovative tools and technologies that have the potential to alter the customer experience, corporate operations, and the workforce.

This is a multi-faceted role, and any significant data engineer could find themselves performing a range of tasks on any day of the week. The average wages of a prominent and good data engineer range from 8 to 13 lakh rupees. However, a data scientist looks at the business more strategically than an artificial intelligence engineer.

Conclusion

We examined all of the nuances of the two subjects and how they are used interchangeably in this Data science vs AI blog. Artificial intelligence and extensive data engineering are witnessing a significant increase in demand in the employment market as we approach 2021.Data Science is concerned with the computations conducted on data, whereas AI is the technology that creates predictions based on the data. Both career fields are in very high demand in todays 21st industry. You are meant to work as a prominent data engineer if you are purely interested in data and extensive data management. However, before deciding between the two, you should confirm your interests and preferences.

The only thing you need to know is your chosen field. Artificial intelligence engineering is a better fit for you if you enjoy working with different teams and want to work with clustered data. If you enjoy data analysis, Data Science may be a good fit for you; but, if you enjoy the principles of AI and the enormous potential it contains, a job in the same sector may be a better fit for you.

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Data Scientist vs. Artificial Intelligence Engineer: Which Is a Better Career Choice? - Latest Digital Transformation Trends | Cloud News - Wire19