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Artificial Intelligence (AI) Certification: In-Depth Guide 2024

Artificial intelligence (AI) may be one of the most wanted skill of the future, thanks to the widespread use of AI technologies. IDC expects total revenues of AI market to be $554 billion by 2024.

There is a skill shortage in the industry for AI skills, however there is also a gap regarding ethical assessments of AI systems’ outcomes. As use of AI systems becomes widespread across many industries, concerns about their bias are also increasing. Standardization and certification may be a solution to fill this gap.

The certification of artificial intelligence (AI)covers two areas:

  • professional artificial intelligent (AI) or machine learning (ML) certifications for people who want to become AI professionals. These programs support professionals to obtain proficiency skills in artificial intelligence.
  • Standardization for AI systems. This is a new agenda in the AI domain and we summarized the current state and potential future developments.

How does a professional get an AI certificate?

In the sector, there are institutions that provide professional certificate programs with trainings consisting of artificial intelligence, machine learning and deep learning. These programs are designed for professional at all levels (e.g. foundation, intermediate and advanced levels) who want to learn or develop artificial intelligence skills. The professional can choose an appropriate program depending on her/his experience in AI domain. Formal education and experience are required for some advanced programs. You can find information about some of these institutions that provide AI certification below:

  • Artificial Intelligence Board of America (ARTIBA) provides Artificial Intelligence Engineer Certification that is built on its international AI & ML Design & Engineering Excellence Framework (AMDEX). The program covers machine learning, deep learning, supervised, unsupervised and reinforced learning, etc. They provide certification programs for different levels of formal education (e.g. bachelor’s degree, master’s) and work experience of the student.
  • IBM provides online and instructor led training about AI. IBM has also launched a new AI certification program, AI Enterprise Workflow Certification for data scientists. Participants can learn about AI workflows, improve their skills and apply beneficial use cases to the businesses. They need to take an exam to become AI Certified by IBM.
  • Google AI provides different education sources and courses for AI professionals. Participants can find technical information and samples to gain new machine learning skills for foundation and advanced and AI projects.
  • MIT provides core and elective courses for machine learning and deep learning applications. Its programs also cover math, statistics and data analysis disciplines. Professionals with a bachelor’s degree such as in computer science, statistics, physics, or electrical engineering need at least three years of experience to attend its professional certification program in AI and ML.
  • Stanford has different online AI and ML trainings according to participants’ experience levels and career plans. For example, prerequisites of its Artificial Intelligence Professional Program designed for working professionals include knowledge in Python, calculus, linear algebra and probability theory.
  • Online education platforms like Coursera and Udemy also include numerous courses on AI and ML. Coursera was founded by Andrew Ng who is a prominent AI researcher from Stanford and provides possibly the most popular course on deep learning.

What are the standardization activities / certifications for artificial intelligence systems?

To prevent unethical or unfair AI systems impacting people’s lives, countries are taking steps to standardize/certify AI applications.

As use of AI applications are increasing in important areas such as healthcare, financial services, recruitment and retail, countries want to take measures for ethical, fair AI systems. There are concerns about bias in predictions, data privacy and cybersecurity. AI solutions need significant amounts of data to train algorithms and make predictions. This means collecting and analyzing personal data, many images and audio data. Some standardization organizations and policymakers are working on ethical AI standards.

  • EU has announced its proposal approach to AI in April, 2021. EU is planing to apply the regulation the second half of 2022 with a transitional period. In the transitional period, the standards would be developed. The regulation would become full applicable with the final standards and conformity assessments the second half of 2024. This approach builds on EU’s guideline on ethics in artificial intelligence in 2019. This guideline included recommendations about ethical rules, implementation challenges and possible actions for AI technologies in the EU. According to the guideline,
    • Key requirements are human oversight, reliable systems and software, privacy and data governance, transparency, avoiding unfair bias and accountability (i.e. independent audits) to build trustworthy AI domain.
    • Further actions may be soft law guidance, hard law legislation and standardization for these requirements.
    • Standardization can play a key role and help AI domain to deploy ethical AI best practices.
    • Ethical standards are voluntary like technical standards. Some researchers/experts claim that standards can be a requirement for AI procurement contracts.
  • Institute of Electrical and Electronics Engineers (IEEE): For AI applications, the organization has projects and working groups to publish some standards which can be used a key reference for policymakers, professionals and firms, such as
    • IEEE P7000™ – Standard for Model Process for Addressing Ethical Concerns During System Design
    • IEEE P7001™ – Standards for Transparency of Autonomous Systems
    • IEEE P7002™ – Standard for Data Privacy Process
    • IEEE P7003™ – Standard for Algorithmic Bias Considerations
  • The International Organization for Standardization (ISO) and The International Electrotechnical Commission (IEC:) Joint technical committee’s works are still continuing to develop standards under the name of ISO/IEC JTC 1/SC 42. The committee aims to support companies that provide AI products and services to set more resilient, reliable and accurate and secure AI systems. The following image shows ISO and IEEE other related works and JTC 1 subcommittees.
  • Global Partnership on AI (GPAI): A multi stakeholder initiative was set up in June 2020 with 15 countries. It aims bringing together related organizations and experts for supporting cutting-edge research and applied activities on AI-related priorities. Its projects support to build trustworthy AI systems.
  • OECD AI Policy Observatory: It is the first intergovernmental standard on AI and adopted in May 2019 by OECD countries. It aims to provide international collaboration on AI public policy issues. Its website publishes detailed information for AI policy initiatives of over 60 countries.
  • The Institute for Ethical AI & Machine Learning: It is a UK based research centre which aims to provide technical research for AI processes and frameworks. The primary target of institute is helping players in AI domain to build reliable machine learning systems. Some of its projects are
    • AI Procurement Framework
    • AI Explainability  Tooling
    • Machine Learning Open Source Software (OSS) Ecosystems
  • Lernende Systeme -Germany’s Platform for Artificial Intelligence: The platform published a discussion paper called Certification of AI Systems in 2020. According to the paper, different types of certification can be formed for AI systems like product certification or a mixed form of product and process certification. The framework of certifications can have important minimum and additional criteria, for example
    • Minimum Criteria: Transparency, functional security, data privacy and equity, avoiding non-discrimination, and etc.
    • Additional Criteria: Open interfaces, being human-centered, sustainability, and etc.

Feel free to read our other articles about artificial intelligence:

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This article was drafted by former AIMultiple industry analyst Ayşegül Takımoğlu.

Access Cem's 2 decades of B2B tech experience as a tech consultant, enterprise leader, startup entrepreneur & industry analyst. Leverage insights informing top Fortune 500 every month.
Cem Dilmegani
Principal Analyst
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Cem Dilmegani
Principal Analyst

Cem has been the principal analyst at AIMultiple since 2017. AIMultiple informs hundreds of thousands of businesses (as per similarWeb) including 60% of Fortune 500 every month.

Cem's work has been cited by leading global publications including Business Insider, Forbes, Washington Post, global firms like Deloitte, HPE, NGOs like World Economic Forum and supranational organizations like European Commission. You can see more reputable companies and media that referenced AIMultiple.

Throughout his career, Cem served as a tech consultant, tech buyer and tech entrepreneur. He advised businesses on their enterprise software, automation, cloud, AI / ML and other technology related decisions at McKinsey & Company and Altman Solon for more than a decade. He also published a McKinsey report on digitalization.

He led technology strategy and procurement of a telco while reporting to the CEO. He has also led commercial growth of deep tech company Hypatos that reached a 7 digit annual recurring revenue and a 9 digit valuation from 0 within 2 years. Cem's work in Hypatos was covered by leading technology publications like TechCrunch and Business Insider.

Cem regularly speaks at international technology conferences. He graduated from Bogazici University as a computer engineer and holds an MBA from Columbia Business School.

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