Category: General News

Socially Responsible Automation



Socially responsible automation (SRA) is a vision, concept, and framework to address the strong need to shape the future development of automation to help create a better world for people and society.

The past few decades have witnessed significant strides in the adoption and proliferation of automation spurred by technological advances in computing, sensing, networking, and communications. Breakthroughs in artificial intelligence (AI) and machine learning, which may currently be the most important general-purpose technologies (Brynjolfsson and McAfee 2017), have broadened the scope of automation beyond mechanized labor and industrial robotics to knowledge work and cognitive agents. Machines increasingly not only perform repetitive, routine tasks in predictable environments but also are being deployed to make complex judgments and solve problems that typically require human intelligence and understanding.


Manufacturing automation, in particular, has significantly affected the employment, productivity, and economic performance of companies and nations. As automation begins to impact knowledge work and the services sector, effects on the global workforce will be even more profound. Although, given the many comparative advantages that humans have, the scope of full substitution of human jobs by automation is likely to remain bounded, at least for the foreseeable future (Atkinson 2017; Autor 2015; Bughin et al. 2017), worker displacement, demand for newer skills, and the continued evolution of work-supplying organizations are inevitable as automation technology develops.

A recent report from the National Academies of Sciences, Engineering, and Medicine (NASEM 2017) discusses in depth the impact of information technology (IT) and automation on the US workforce. While automation, in conjunction with globalization, trade, and economic policies, has been a strong contributing factor to lower employment ratios and increased income inequalities over the past few decades, it is not just the technologies themselves but the choices made around them that have driven these impacts. Noting, for example, that advances in internet and communication technologies paved the way, in a manner unforeseen, for the outsourcing and offshoring of business work, the report notes that organizational decisions, power structures, and ideologies ultimately shape the outcomes of technologies for the workforce, society, and economy. And “technologists, policymakers (such as private-sector managers and public officials), and other leaders have the power to design IT and deploy it for the benefit of society, driven by a broad discussion of what impacts are desirable and a deeper understanding of how design, deployment, and policy decisions can achieve these impacts” (NASEM 2017, p. 138).

Similar sentiments are echoed in a report of the IEEE (2016) Global Initiative for Ethical Considerations in Artificial Intelligence and Autonomous Systems. -Citing the technology community’s lack of awareness and ownership of socioeconomic concerns surrounding automation, the report urges all those “involved in the research, design, manufacture, or messaging” of autonomous systems and AI to go beyond the search for more computational power or the attainment of purely functional goals and technical solutions (IEEE 2016, p. 3). It calls on them to place human well-being, empower-ment, and prosperity at the core of their pursuits, and to ensure that technology choices are “-thoroughly scrutinized for social costs and advantages that will also increase economic value for organizations by embedding human values in design” (IEEE 2016, p. 36).

Socially Responsible Automation (SRA):
Four-Level Model

Motivated by the above considerations, we introduce the vision, concept, and framework of socially responsible automation to help technologists and business leaders drive the evolution of automation for societal good. This aspirational vision is grounded on two principles:

  1. humans will and should remain critical and central to the workplace of the future, controlling, complementing, and augmenting the strengths of technological solutions; and
  2. automation, artificial intelligence, and related technologies are but tools to improve and enrich human lives and livelihoods.

Our definition of automation encompasses mechanized physical labor as well as information-based cognitive work (“knowledge work”) and combinations of these. Also, while the term “human-centric” (or “human-centered”) automation has been used by some researchers (e.g., Oishi et al. 2016) in the context of safety and efficiency of human-technology interaction in semiautonomous systems, we use human-centric to refer to approaches that broadly support the professional, social, and economic well-being of humans in a world of ubiquitous automation.

Figure 1

We define, describe, and illustrate the SRA vision using a four-level conceptual model that captures current industry practices as well as envisioned future approaches to automation. The SRA pyramid (figure 1) provides a simple but powerful visual aid for guiding automation strategy development.

Level 0: Cost-Focused Automation

At the lowest level of the model are approaches to automation that are predominantly cost-focused: economic benefits from labor reduction drive technology decisions. Such cost-based programs are not only not socially conscious or human-centric, they also often fail to deliver, are unsustainable, or even end up being -detrimental to business interests.

Consider, for example, the business process out-sourcing (BPO) industry whose core business model is based on labor arbitrage and the availability of inexpensive human capital in developing countries. Rising costs of doing business in once preferred destinations such as India and China have driven increasing interest in new technologies such as robotic process automation (RPA), the use of software “bots” (IRPAAI 2015) for repetitive, high-volume tasks. Considered a disruptive trend, RPA holds tremendous promise for the BPO industry. However, success has so far been limited (Edlich and Sohoni 2017; Rutaganda et al. 2017) in part because of (1) a piecemeal approach to automation that fails to address systemwide implications and outcomes; (2) failure to account for the subtle but vital roles of humans in -handling complex, nonstandard, and changing situations; and (3) the (hidden) costs of automation itself.

Level 1: Performance-Driven Automation

At the next level of automation, productivity and other performance metrics such as accuracy, scalability, speed, quality of service, and flexibility drive design and technology choices. Performance-focused approaches address several of the shortcomings of Level 0 automation by taking an end-to-end system view that is cognizant of the role of the human in the loop. Processes and systems are reengineered to take advantage of the benefits of automation while leveraging human skills and capabilities to supplement and overcome the limitations of technological solutions.

As an example, consider the retail giant Amazon’s judicious integration of human and machine skills in its warehouses, where employees “pick, pack, and stow” goods while robots handle the transportation of loaded bins and shelves. Thus, robots do the routine tasks and “heavy lifting” that they are best suited for and humans perform tasks that require dexterity and flexibility that robots cannot yet do. This large-scale automation is reported to have resulted in significant reductions in “click to ship” cycle times and operating costs (Wingfield 2017).

Level 1 automation approaches move beyond cost efficiencies, but they are still driven primarily by business metrics without taking account of workforce implications or the societal costs and benefits of technology.

Level 2: Human (Worker)-Centered Automation

Human-centered automation approaches explicitly acknowledge and emphasize the critical and valuable role of people in human-machine cooperative systems. They are based on the idea that the ultimate goal of automation is not to sideline people or replace them with machines but to encourage new forms of human-technology interaction, augment human capabilities, and create new roles for people. The business goals are not just performance optimization but also worker development and enrichment. In comparison to the previous two levels, Level 2 automation is not technology-centric but, as the term makes clear, worker-centric. It is the first step in socially responsible automation practices.

Toyota exemplifies the adoption of human-centric automation practices with its philosophy that “robots are not the strategic centerpiece, but merely enablers and handmaidens, helping assemblers do their jobs -better, stimulating employee innovation and when possible facilitating cost gains” (Rothfeder 2017). On Toyota’s manufacturing lines, workers don’t just troubleshoot and fix problems; they produce goods manually first, then continually innovate and simplify processes; once they perfect a process, the machines take over. In some cases Toyota has even eliminated automation so that workers retain their core expertise and skills and remain cognizant of the criticality of their roles in the company’s mission.

Far from considering human workers as an expense to be avoided, Level 2 approaches leverage human capabilities to derive more business benefits in a manner that is workforce empowering. However, strategies and choices are still viewed within the sphere of the organization and not those of the broader business-society ecosystem.

Level 3: Socially Responsible Automation

At the highest level of the model is SRA: the technology choices, business strategies, innovation approaches, and management practices that move the affordances of automation beyond cost and performance efficiencies toward profitable and sustainable growth, with more and better jobs driving economic development and social cohesion. Thus, SRA centers on two core goals: driving growth through automation while promoting both economic performance and societal well-being.

Automation is inherently labor-reducing: “the structural dynamics of the economic system inevitably tend to generate what has rightly been called technologicalunemployment. At the same time, the very same structural dynamics produce counter-balancing movements which are capable of bringing the macroeconomic condition [of full employment] toward fulfilment, but not automatically” (Pasinetti 1981, p. 90; emphasis in original). While productivity gains from automation may lead to increased demand for a company’s goods and services—increasing, in turn, the demand for labor—such outcomes occur only under the right conditions of labor supply, income levels, and demand for goods (Autor 2015).

Realizing the goals of SRA therefore will require explicit, active interventions, such as economic policies (Pluess 2015), and/or, as we suggest, simultaneously exercising the twin levers of automation and innovation. In other words, proactive, conscientious, and systematic identification of opportunities for new revenue streams and job-enabling growth should be an integral part of a business’s automation strategy while leveraging the cost efficiencies and operational enhancements that automation provides.

Toyota is a great example, not just for human-centric automation but also for its SRA practices. The company’s sustained growth and competitive positioning as an industry leader are the result of a judicious combination of the use of automation, innovation, and sound management practices. Toyota’s strategy does not primarily target labor to reduce production expenses but instead is based on the smart use of materials, the design of parts to maximize performance and fuel efficiency, a platform-based approach for more economical global sharing of engine and vehicle models, and emphasis on lean processes that enable zero-downtime flexible manufacturing. With these measures, the company has continued to enjoy the top spot in sales and industry profit margins through the years, along with an expanding global workforce (Rothfeder 2017).

The next example is one that has become a poster child for the success of small business manufacturing in the automation era (Fishman 2013). Faced with declining demand for its products and rising competition, Marlin Steel, once the “king of the bagel baskets,” reinvented itself through a series of remarkable measures. It made significant forward-looking investments in robotics and automation; reengineered its production processes; enhanced its product line to manufacture high-value, highly engineered custom metal wire products; and expanded its client base to new markets and customers. In addition to these structural measures, it invested in its people, equipping them with the skills and training necessary to survive and grow in the new technology-driven workplace. By taking this -innovation-driven, business-focused, human-centric, and responsible approach to automation, Marlin Steel has grown in its revenue, competitiveness, and employee base.

Our four-level construct may remind the reader of Carroll’s (2016) pyramid, the well-known model of corporate social responsibility (CSR). Indeed, our aspirational view of SRA is guided by the literature on CSR, a rich and mature field with theoretical underpinnings in the disciplines of business ethics, economics, and moral philosophy (Godfrey and Hatch 2007). In particular, we believe that SRA aligns best with the stakeholder theory of CSR. We also note the connection between SRA and ethical AI, which addresses a broader set of values (e.g., human rights, fairness, bias, transparency, and privacy; IEEE 2016) beyond the labor and workforce implications that are our focus in this paper.

Realizing the SRA Vision

Realizing the goals of SRA requires an organization’s development and implementation, at many levels, of robust business, innovation, design, and technology strategies that are all aligned with and reinforce each other (figure 2). Drawing from a variety of disciplines—business ethics, innovation management, and sociotechnical systems design—we highlight below selected frameworks and methodologies relevant to each of these strategic planks.

Figure 2 

Business (Ethics) Strategy

A high-level business strategy for SRA begins with the question, “How can we fuel growth and enable job creation through automation?” To move automation beyond cost and performance efficiencies toward profitable, sustainable business growth with more and better jobs, the SRA approach identifies ways to (1) align a firm’s commercial interests with societal values and (2) make social goals integral to an organization’s core business model.

In a highly cited Harvard Business Review article, -Porter and Kramer (2011) propose the principle of shared value: the idea of creating economic value in a way that also creates value for society. In this view societal needs, not just traditional economic needs, define markets, and the purpose of a corporation is to create shared value, not just profits. Companies that better connect their success with societal improvement open new avenues for innovation, new products, and new customers, all of which expand markets, create differentiation, and drive economic value and growth.

SRA can be thought of as an instantiation of the shared value concept in the context of automation. In this case, the shared value principle would guide firms to ask the following questions:

  • How can we leverage the -efficiencies gained by automation to tap into newer markets, revenue streams, and -customers?
  • How do we identify, enhance, -channel, and leverage the critical value, both hidden and transparent, that our -workers provide?
  • How will specific technology choices affect employment and the societies where we operate?
  • If economic efficiencies of technologies are comparable, which would have the least negative social impact, and which would maximize -community -benefit?

Our thinking on SRA is also influenced by the “common good” principle in -ethics (-Velasquez et al. 1992). With roots in the writings of -philosophers such as -Plato, Aristotle, and Cicero, a contemporary definition of common good comes from the political and moral -philosopher John Rawls (1999, p. 233): “maintaining conditions and achieving objectives that are similarly to everyone’s advantage.” While not without its challenges (-Velasquez et al. 1992), the common good principle not only provides a framework for technologists to consider the values supported—or compromised—by their choices but also helps them formulate and articulate the rationale for their decisions, which is key for stakeholder transparency (IEEE 2016). For other -ethics-based approaches that may be more suitable for specific organizations and situations of automation deployment, we refer the reader to Velasquez and colleagues (2009).

Innovation Strategy

Driven by and closely aligned with a firm’s business strategy are its innovation goals. Sustained job creation, at the heart of SRA, requires innovations of many kinds beyond the commonly recognized forms of product and process innovations. Sawhney and colleagues (2006) identify 12 ways for companies to innovate, with concrete examples of successful innovation strategies that leverage more than one and often several of these dimensions. The 12 categories for innovation are anchored by offerings, customers, processes, and presence (the who, what, how, and where of the business), supplemented by platform, solutions, customer experience, value capture, organization, supply chain, networking, and brand.

Figure 3 

Both Marlin Steel and Toyota leverage innovations in product, process, and organization (i.e., changing a firm’s form, function, or activity scope, including employee roles and responsibilities) as part of their growth strategy. We note further -Marlin Steel’s successful use of customer innovation (discovering new customer segments) and Toyota’s platform innovation (using common components or building blocks to create derivative offerings), all alongside their core -automation efforts. Figure 3 provides an illustrative representation of the multi-dimensional innovation profile of these two companies using the innovation radar devised by -Sawhney and colleagues (2006).[1]

Finally, we note that in this era of digitalization and the fourth industrial revolution, automation has even stronger potential to drive growth by enabling smart products and smart services.

Design Strategy

Key to developing and implementing a robust SRA program is a broad systems design perspective. As depicted in figure 4, the “system” scope progressively expands at higher levels of the pyramid, from the physical and software infrastructures to human-technology integrated work environments to the business and social ecosystems supported and impacted by the technology. This calls for suitable systems design philosophies and approaches, two of which we highlight here.

Figure 4 

Value-sensitive design (VSD), a concept that originated in computer ethics, “is a theoretically grounded approach to the design of technology that accounts for human values in a principled and comprehensive manner throughout the design process” (Friedman et al. 2008, p. 70). VSD is an iterative approach that involves identifying stakeholders affected by the technology; understanding their views, preferences, and behaviors through quantitative and qualitative social science methods; and studying how specific technologies in specific contexts support or harm human values.

Another powerful systems design approach for automation is what Autor (2015, p. 23) characterizes as environment reengineering, the process of “radically simplify[ing] the environment in which machines work to enable autonomous operation.” The “design for automation” philosophy is exemplified by Amazon’s retail automation, robotic surgeries, and business process reengineering (Hammer 1990) in the services industry, where workflows and environments are redesigned to optimally leverage the complementary skills of robots and humans.

Technology Strategy

We highlight here some research challenges broadly categorized under human-technology cooperative work and integrated design tools and environments to support socioeconomically optimal technology choices for automation.[2] The first category concerns problems primarily at the intersection of control theory and cognitive sciences; these include optimal task allocation between humans and automated processes, real-time feedback control and adaptation in a cyber-human shared governance model, fail-safe operation of semiautonomous systems, and adaptive software systems for work automation. A quick scan of relevant literature indicates that many of these problems are beginning to be addressed in various technical communities.[3]

For integrated design tools, we believe that frameworks such as the Digital Twin that enable modeling, analysis, and evaluation of design choices in the manufacturing domain can be effectively extended to support both the design of human-technology collaborative environments and the evaluation of technology alternatives. However, significant work remains to be done to include rich human behavior modeling, worker performance modeling, and socioeconomic analysis in this framework. To the best of our knowledge, no such integrated paradigms exist outside the manufacturing domain for knowledge work automation.


As Rotman (2017) writes, “The economic anxiety over AI and automation is real and shouldn’t be dismissed. But there is no reversing technological progress.” The key is to implement measures that enable everybody to benefit from these transformative technologies and turn AI and automation into forces for shared prosperity.

In this paper we aim to help technologists and business leaders realize this vision by providing a comprehensive framework that looks beyond today’s prevailing practices and provides a systematic, structured way to frame choices, assign priorities, and design robust strategies. We also discuss the indispensable role of innovation in realizing SRA and, with examples, show that, as with CSR, there is a clear business case for SRA. We hope to inspire and help shape a future where automation and AI work for all.


Atkinson RD. 2017. In defense of robots. National Review LXIX(7), April 17.

Autor DH. 2015. Why are there still so many jobs? The history and future of workplace automation. Journal of Economic Perspectives 29(3):3–30.

Brynjolfsson E, McAfee A. 2017. The business of artificial intelligence. Harvard Business Review, July.

Bughin J, Manyika J, Woetzel J. 2017. A Future That Works: Automation, Employment, and Productivity. McKinsey Global Institute.

Carroll AB. 2016. Carroll’s pyramid of CSR: Taking another look. International Journal of Corporate Social Responsibility 1(3).

Edlich A, Sohoni V. 2017. Burned by the Bots: Why -Robotic Automation Is Stumbling. New York: McKinsey & -Company.

Fishman C. 2013. The Road to Resilience: How Unscientific Innovation Saved Marlin Steel. New York: Fast Company.

Friedman B, Kahn PH, Borning A. 2008. Value sensitive design and information systems. In: The Handbook of Information and Computer Ethics, eds Himma KE, Tavani HT. Hoboken NJ: John Wiley & Sons. pp. 69–101.

Godfrey PC, Hatch NW. 2007. Researching corporate social responsibility: An agenda for the 21st century. Journal of Business Ethics 70(1):87–98.

Hammer M. 1990. Reengineering work: Don’t automate, obliterate. Harvard Business Review, July-August.

IEEE [Institute of Electrical and Electronics Engineers]. 2016. Ethically Aligned Design: A Vision for Prioritizing Human Wellbeing with Artificial Intelligence and Autonomous Systems. Piscataway NJ: IEEE Global Initiative for Ethical Considerations in Artificial Intelligence and Autonomous Systems.

IRPAAI [Institute for Robotic Process Automation and Artificial Intelligence]. 2015. Introduction to Robotic Process Automation: A Primer. Online at Process-Automation-June2015.pdf.

NASEM [National Academies of Sciences, Engineering, and Medicine]. 2017. Information Technology and the US Workforce: Where Are We and Where Do We Go from Here? Washington: National Academies Press.

Oishi MMK, Tilbury D, Tomlin CJ. 2016. Guest editorial special section on human-centered automation. IEEE Transactions on Automation Science and Engineering 13(1):4–6.

Pasinetti LL. 1981. Structural Change and Economic Growth. Cambridge: Cambridge University Press.

Pluess JD. 2015. Good Jobs in the Age of Automation: Challenges and Opportunities for the Private Sector. San Francisco: Business for Social Responsibility.

Porter ME, Kramer MR. 2011. Creating shared value. Harvard Business Review, January-February.

Rawls J. 1999. A Theory of Justice. Oxford: Oxford University Press.

Rothfeder J. 2017. At Toyota, the Automation Is Human-Powered. New York: Fast Company.

Rotman D. 2017. The relentless pace of automation. MIT Technology Review, February 13.

Rutaganda L, Bergstrom R, Jayashekhar A, Jayasinghe D, Ahmed J. 2017. Business models: Avoiding pitfalls and unlocking real business value with RPA. Capco Institute Journal of Financial Transformation 46:104–114.

Sawhney M, Wolcott RC, Arroniz I. 2006. The 12 different ways for companies to innovate. MIT Sloan Management Review, Spring.

Velasquez M, André C, Shanks T, Meyer MJ. 1992. The -common good. Issues in Ethics 5(1).

Velasquez M, Moberg D, Meyer MJ, Shanks T, McLean MR, DeCosse D, André C, Hanson KO. 2009. A framework for ethical decision making. Santa Clara: Markkula Center for Applied Ethics.

Wingfield N. 2017. As Amazon pushes forward with robots, workers find new roles. New York Times, September 10.

[1]  The radars in figure 3 are not based on a rigorous assessment of the two companies and are to be interpreted as qualitative representations of their innovation strategies.

[2]  For an excellent overview of fundamental technologies and advances driving the proliferation of autonomous and intelligent systems, see NASEM (2017) and references therein. Brynjolfsson and McAfee (2017) provide a balanced review of the capabilities and limitations of current technologies.

[3]  For example, the “future of work at the human-technology frontier” is one of the National Science Foundation’s 10 Big Ideas ( tech.jsp).


What Does It Mean To Be Intelligent?


Sighing loudly, Jimmy Thompson, the quiet student in the front row, is smoothing his hair back nervously with his left hand. His right hand is tapping his pencil, eraser-side down, on the desk rhythmically. His feet shift under the desk in an absentminded yet patterned restlessness; right ankle over left, left ankle over right, feet flat on the floor; lather, rinse, repeat.

He seems to be having difficulties with the math test.

In the last twenty minutes he has only written down his name, the date, and a pretty spot-on sketch of Master Chief from Halo in the margin. His fidgeting is causing other students’ attention waning from the test, and instead being focused on him. Hoping to help ease his frustration, you walk to his desk, bend down slightly, and whisper: “Are you ok? Do you need help with anything?” He looks up at you, defeated, and says the words no teacher wants to hear: “I’m just too dumb for this.”

What do you do?

If your first instinct is to agree with him, you might need to seek out a different profession. What Jimmy needs now is immediate positive feedback. He needs to be encouraged, and feel that he is worthwhile and understand it is okay to have different strengths and weaknesses than others. Unfortunately, this will be hard to do during a test. The best thing to do at this point is to tell him it is important to try, not to give up, and ask to see him after class for a pep talk.

When Jimmy does come dragging his feet to your desk after class, be prepared for a less than enthusiastic response to your pep talk.  He is feeling bad about himself, frustrated, and possibly angry at you for giving the test in the first place. The hard part is knowing what to say to him to motivate him.

So What Do You Say?

Before you have a talk with Jimmy about his intelligence, or at least his (and yours!) perceived level of intelligence, you need to know what it is first. You might be thinking that it is easy to know who is intelligent and who is not, but therein lies the problem. While many educators and parents equate good grades and study habits with intelligence, doing so can be doing a disservice to many of your students and children.

Have you ever heard someone say: “She’s brilliant in math, but has no common sense”? Or perhaps: “He can’t find his way out of a paper bag, but he makes a mean tiramisu”? While the point of the comment is either to be disparaging or complimentary is unknown, it is an example of different areas and levels of intelligence.

What Do You Know?

If someone were to ask you right now to define the meaning of intelligence, what would you say? What if someone asked you why someone in your class is smart; would you know? Think about it for a second while all of the hundreds of ideas float around in your mind as you quickly try to define it. In a psychology course given at Mercer Community College in New Jersey, a PowerPoint presentation was shown asking students to define intelligence and theories regarding it.

It is actually pretty difficult to explain an individual’s perception of intelligence. You may also find it surprising that your friends and colleagues will define intelligence, or smart, differently than you do.

Intelligence is Unofficially Measured in Many Ways

intelligence quote

This may be startling to some, but intelligence is judged subjectively. This can cause problems when conflicts arise. One person’s genius is another person’s average individual. Many people have never taken an IQ (Intelligence Quotient) test to find out their number, but that is probably a good thing. If we only went by IQ test numbers, then your IQ would have to be listed on your resume, or as this same article suggests, a CAT scan should be performed at every job interview.

This may actually sound like a good idea, especially of a co-worker you find to be lacking, but do not get too excited. While mandatory IQ testing and number revealing could potentially “thin out the herd”, this would not be a true indicator of what the person can actually do.

Intelligence is measured by those around you and also against the individual’s own knowledge of the world. Have you noticed how you are idolized by the younger students who think you know everything there is to know about everything? As far as they know, you do know everything!

Unfortunately, once they hit about twelve years old, you cease to have any grey matter left in your ancient brain, and the same elementary angels are now wondering how you got your shirt buttoned on your own.

It Is All Relative!

Thinking someone is smart or intelligent is not only subjective, but also relative. Someone who is not musically inclined will think the fifteen year old pianist next door is a musical genius. The fifty year old piano professor, however, will most likely be wincing at said fifteen year old’s murderous rendition of  Beethoven’s “Moonlight Sonata” and remind himself to buy better ear plugs at the drugstore.

Pride can also factor in on measuring intelligence. Grandma deems her three year old twin grandsons the smartest toddlers in the history of time for blowing out the candles (along with a mild amount of spit) on their SpongeBob SquarePants birthday cake.

However, every time those same darling three year olds smacks mom in the head with a toy, their perceived intelligence levels go down as mom’s blood pressure rises.

But What Is Intelligence?

Over the years, intelligence has been defined, redefined, summarized, and defined again, but still no one person has the same exact definition or idea. There has even been a paper written called “The Collective Definitions of Intelligence” which include approximately seventy different definitions, or interpretations of what intelligence entails. Seventy! These seventy definitions were analyzed to find a simple informal definition. The one that is used by many comes from S. Legg and M. Hutter and it states: “Intelligence measures an agent’s ability to achieve goals in a wide range of environments.”

“The Collective Definitions of Intelligence” which include approximately seventy different definitions, or interpretations of what intelligence entails. Seventy!

Similarly, the Merriam-Webster Dictionary defines it as “…the ability to learn or understand or to deal with new or trying situations… the skilled use of reason…the ability to apply knowledge to manipulate one’s environment or to think abstractly as measured by objective criteria (as tests).”

The gist is the more intelligent the person, the more they are able to apply what they know to new situations. This sounds fair, but the question remains: how does one become intelligent?

The Bell Curve

Many ideas currently held about intelligence were brought forth in 1994 when the book The Bell Curve was published. It was written by a Harvard psychology professor named Richard Hernstein, and a political scientist named Charles Murray. Although controversial to many researchers, the book grew to popularity of epic proportions. Educators, policy makers, and the general public felt this book was an “aha!” moment and held the ideas and explanations near and dear to their hearts.

Conclusions gained from the book were that intelligence is genetic, IQ tests are accurate, and an individual’s IQ is not affected by living conditions, nurture, or the environment. The points the book also managed to get across were that IQ differences between the races were also genetic, and outside influences, educational or otherwise, did not affect the IQ of a person, or even out the IQ gap between different races.

Do not worry, all of those conclusions have been torn apart, analyzed, and reworked. Educators know very well how outside influences affect a student’s intelligence. If, in fact, it did not make a difference, what would be the point of interventions or initiating programs trying to give students a better chance and to help bridge the gap between races?

What if you learn best by bouncing ideas off another person, gaining important feedback, and there is no one around to do that for you?

The notion of living conditions or the environment not affecting IQ seems absurd when you think about it, but in fairness, it is not if you are coming from an “IQ is genetic” place. Think about where you learn best. Is it in a noisy, crowded, and littered area? It could be, there is no judgment here. But what if you had that type of environment and you learn best in a calm, quiet, clean atmosphere?

Conversely, what if you learn best by bouncing ideas off another person, gaining important feedback, and there is no one around to do that for you? This definitely will affect intelligence. If the tools are not there for a person to learn how to know what to do in different situations, how will they achieve this? The disparity of levels of intelligence has much to do with the conditions one lives in.

Inherited Smarts

While many have believed in the past that “smart breeds smart” naturally, it is not necessarily true. It can be genetic, but to be able to actually measure the amount that is, is a test to be taken in the far, far future. You may often see well educated and intelligent parents having “smart” children, it could be argued that it was just as much environmental influences as genetic. After all, there are intelligent parents with children who are not as bright as they are, well, at least not yet.

Parents that went to college and who were motivated academically are more likely to do the same for their own children. The converse is also true, although many parents work hard so their children can achieve more than they had during their schooling years.

Disparity of IQ

Disparities can often be simply because those with lower socioeconomic status have fewer opportunities. Often lower SES families deal with poverty, poor living conditions, and an environment not conducive to learning. To gain data, this study  followed almost 49,000 mothers and their children, numbering almost 60,000, all in different living conditions and SES from birth to age seven.

Often lower SES families deal with poverty, poor living conditions, and an environment not conducive to learning.

At age seven, the children were given the Wechsler Intelligence Scale for Children test. From this we were able to ascertain that the higher the SES, the less inhibiting environmental factors there were, and the IQ’s were higher than those with lower SES.

The Bell Curve Doesn’t Ring as Loud

Now that the findings of The Bell Curve have been corrected, it is time to get educators, parents, and policy makers to get on board. Hopefully, those still thinking the original findings are right are few and far between. Not to disregard the conclusions in their entirety, it was correct that tests can determine IQ, but not like people may think.

IQ Tests

IQ tests can provide a function, but the score should not be used as a defining factor of complete intelligence. These tests can be a predictor of future success, but not a guarantee. IQ tests actually measure two types of intelligence: fluid and crystallized. The intelligences work together, but where crystallized intelligence cannot affect fluid, the fluid intelligence can affect crystallized.

Fluid Intelligence

Fluid intelligence is monitored by the prefrontal cortex and measures ability to predict patterns, problem-solving, and learning. It can be affected by amount of working memory (where the thinking happens) and the ability to focus attention. Because this intelligence is learning-based, it remains level until middle adulthood, then begins to wane. Based on this definition, you can see where a student with ADD or ADHD can have lower scores, even if they are highly intelligent.

[Click here to find out more about engaging students with ADHD.]

Crystallized Intelligence

Crystallized intelligence is monitored by many different parts of the brain and measures the knowledge about the world, and the understanding that things can change, be changed, added upon, and subtracted. It is also includes language, reading comprehension, and vocabulary. Crystallized intelligence is ever changing, and builds throughout one’s life.

Multiple Intelligences

While these two types of intelligence are measured, there are many different forms of intelligence. Dr. Howard Gardner, a name most educators are familiar with, developed the Theory of Multiple Intelligences in 1983. There are nine different intelligences that people have, and many have more than one.

They are: linguistic, logical-mathematical, music rhythm, bodily-kinesthetic, spatial, naturalist, interpersonal, intrapersonal, and existential intelligences. Everyone has something they are good at doing or learning, and it can be defined by one of the intelligences. Some people also have social and emotional intelligence.

If these intelligences could be measured formally, many people would have a much higher number on their IQ score. Think about someone in your class that struggles, then see if you can find what they are good at doing based on the list above. Put yourself, as an educator to the test, and see where you fall in the Multiple Intelligences.

Hopefully, one is interpersonal, which is dealing with others.

On Being Smart Vs Intelligent

As with intelligence, the word “smart” is heard, and the two are often interchangeable. The difference is that being smart is more about being adaptable. Do you know someone who is more successful than you (not narrowly defined by money) and never went to college? They may not be formally educated, but they were smart and did something right.

Someone can be book-smart and effortlessly regurgitate what they have read, while others learned how to fix cars and electrical devices by watching others.

So, Back to Jimmy…

Here comes Jimmy, shuffling his feet, dreading the talk with you about his test. What are you going to say? Not to be ominous, but this could be a defining moment for Jimmy. We all have one teacher we look back on fondly for something, and it is usually because of the way they treated us, believed in us, and made learning fun.

Remind Jimmy of what he can do, and do not focus on what he cannot do. [Read more about the right way to give student feedback]

Point out Jimmy’s artwork to let him know you would appreciate it even more on a large piece of paper, rather than the test. Remind him of what a good artist he is, and ask what he feels he can do well. You may be surprised to find that this quiet, frustrated boy that cannot do his math problems, is more intelligent than you think.



5 Responses

  1. Alex M. says:

    I think self-awareness is a real show of intellect – being aware of others and your impact on the world around you. Creativity is key, too, such as with authors and musicians (composers, particularly) – being able to understand human emotions and convey an incredible story. Obviously if you’re academically gifted that’s great, but there’s much more to intelligence than simply getting straight As in your exam results.

  2. Marilyn says:

    What it means to be intelligent, as, for my opinion is a combination of being street smart, and book smart. They work together.

  3. Jglow says:

    I wanted to know what it means to be intelligent. I kept reading and reading and reading, and still I don’t understand. I must not be very intelligent.

  4. Filiberto R says:

    My mother told me that I am intelligent, and I look up almost everything people tell me. I saw the Google definition of intelligent it said Smart. I looked up this article, and it taught me as I read it all. I.Q. test doesn’t determine my intelligence. Also what is intelligence? How do you get intelligence? I learned that if you can do one thing or more in seventeen ways or more you are intelligent. Correct me if I am wrong on that last sentence. Anyway I love hearing from another person that I am intelligent. Especially someone I know is genuin